Plug-In Hybrid Vehicle Analysis: Milestone Report

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Plug-In Hybrid Vehicle Analysis Milestone Report NREL/MP-540-40609 November 2006

T. Markel, A. Brooker, J. Gonder, M. O’Keefe, A. Simpson, and M. Thornton National Renewable Energy Laboratory

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 Plug-In Hybrid Vehicle Analysis Milestone Report NREL/MP-540-40609 November 2006

T. Markel, A. Brooker, J. Gonder, M. O’Keefe, A. Simpson, and M. Thornton National Renewable Energy Laboratory

Prepared under: Task No. FC06.2000 B&R Code VT0103000

Prepared for the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy

In fulfillment of: FreedomCAR and Vehicle Technologies Program September 2006 Milestone/Deliverable 6.3 Grid-Connected Hybrid Vehicle Efficiency Improvement Analysis

National Renewable Energy Laboratory 1617 Cole Boulevard, Golden, Colorado 80401-3393 303-275-3000 • www.nrel.gov Operated for the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy by Midwest Research Institute • Battelle Contract No. DE-AC36-99-GO10337 NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

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Preface

The Vehicle Systems Team in the Center for Transportation Technologies and Systems at the National Renewable Energy Laboratory (NREL) performed this work. This report documents completion of the September 2006 milestone as part of NREL’s 2006 FreedomCAR and Vehicle Technologies (FCVT) Annual Operating Plan with the U.S. Department of Energy (DOE). The objective of the work was to provide objective systems simulations and analysis of plug-in hybrid electric vehicle technologies.

This report supports the goals of the DOE/FCVT Program to quantify the potential benefits of plug-in hybrid electric vehicle technology through analysis and modeling. Specifically, this effort supports Task 1: Modeling and Simulation, discussed in the FCVT Multi-Year Program Plan.

This work was funded by the Advanced Vehicle Technology Analysis and Evaluation activity in support of the FCVT Program of the Office of Energy Efficiency and Renewable Energy within the U.S. DOE. We wish to thank our sponsor, Lee Slezak, for his guidance and support. Terry Penney, as NREL’s FCVT technology manager, and Matt Thornton, as task leader for NREL’s Vehicle Systems Analysis Task, supported this project.

We would also like to express our appreciation to the members of the FreedomCAR Vehicle Systems Analysis Technical Team: Larry Laws (GM), Mark Biernacki (DaimlerChrysler), and Asi Perach (Ford) for providing technical insight and industry review.

Tony Markel, project leader

iii Table of Contents

Executive Summary

Section 1: Plug-In Hybrid Electric Vehicle Fuel Economy Reporting Methods

Section 1.1: “Measuring and Reporting Fuel Economy of Plug-In Hybrid Vehicles,” presented to the Vehicle Systems Analysis Technical Team, Feb. 1, 2006 Section 1.2: “Measuring and Reporting Fuel Economy of Plug-In Hybrid Electric Vehicles,” published at the 22nd International Battery, Hybrid, and Fuel Cell Electric Vehicle Symposium and Exposition, October 2006

Section 2: Plug-In Hybrid Electric Vehicle Cost and Consumption Benefit Analysis

Section 2.1: “Cost/Benefit Analysis of Hybrid-Electric and Plug-In Hybrid-Electric Vehicle Technology,” presented to the Vehicle Systems Analysis Technical Team, March 1, 2006 (updated May 2006) Section 2.2: “Cost-Benefit Analysis of Plug-In Electric Vehicle Technology,” published at the 22nd International Battery, Hybrid, and Fuel Cell Electric Vehicle Symposium and Exposition, Oct. 26, 2006 Section 2.3: “Plug-In Hybrid Electric Vehicles: Current Status, Long-Term Prospects, and Key Challenges,” presented at the Federal Network for Sustainability Meeting, July 20, 2006 Section 2.4: “Plug-In Hybrid Electric Vehicle Energy Storage Analysis,” presented to the FreedomCAR Electrochemical Energy Storage Technical Team Plug-In Hybrid Electric Vehicle Battery Working Group Meeting, July 25, 2006 Section 2.5: “Plug-In Hybrid Modeling and Application: Cost/Benefit Analysis,” presented at the Third AVL Summer Conference on Automotive Simulation Technology, Aug. 24, 2006

Section 3: Plug-In Hybrid Electric Vehicle Real-World Performance Expectations

Section 3.1: “Plug-In Hybrid Electric Vehicles: Current Status, Long-Term Prospects, and Key Challenges,” presented at the Clean Cities Congress and Expo, May 8, 2006 Section 3.2: “Plug-In Hybrid Vehicle Real-World Performance Expectations,” presented to the Vehicle Systems Analysis Technical Team, June 14, 2006 Section 3.3: “Using GPS Travel Data to Assess the Real-World Driving Energy Use of Plug-In Hybrid Electric Vehicles,” to be published at the Transportation Research Board 86th Annual Meeting, February 2007

iv Section 4: Plug-In Hybrid Electric Vehicle Energy Management Strategies

Section 4.1: “Plug-In Hybrid Electric Vehicle Energy Storage System Design,” presented at the Advanced Automotive Battery Conference, May 19, 2006 Section 4.2: “Plug-In Hybrid Electric Vehicle Energy Storage System Design,” published at the Advanced Automotive Battery Conference, May 19, 2006 Section 4.3: “Dynamic Programming Applied to Investigate Energy Management Strategies for a Plug-In Hybrid Electric Vehicle,” to be published at the 22nd International Battery, Hybrid, and Fuel Cell Electric Vehicle Symposium and Exposition, October 2006

Summary

v Plug-In Hybrid Vehicle Analysis Milestone Report September 2006 Tony Markel Aaron Brooker, Jeffrey Gonder, Michael O’Keefe, Andrew Simpson, Matthew Thornton

In fulfillment of FreedomCAR and Vehicle Technologies Program September 2006 Milestone/Deliverable 6.3 Grid-Connected Hybrid Vehicle Efficiency Improvement Analysis

Executive Summary

NREL’s plug-in hybrid electric vehicle (PHEV) analysis activities have made great strides in FY06 to objectively assess PHEV technology, support the larger U.S. Department of Energy PHEV assessment effort and complementary activities at other national laboratories, and share technical knowledge with the vehicle research community and vehicle manufacturers through the FreedomCAR Vehicle Systems Technical Team and the Electrochemical Energy Storage Technical Team. The key contributions of this activity include:

1. Proposed improvements to the existing test procedure for reporting PHEV fuel economy 2. A thorough exploration of the PHEV design space, including an evaluation of the trade-offs between cost and fuel consumption 3. The application of real-world driving data to quantify the impacts of travel behavior on the potential benefits of PHEVs 4. The optimization of energy management strategies focusing on petroleum displacement.

The NREL research team has participated in many key industry meetings, and its research has been documented in eight formal presentations and five technical papers that have been published or have been submitted for publication within the next 6 months. This milestone report is a compilation of these papers and presentations for future reference.

The following is a summary of important insights that emerged from the four areas of emphasis.

Plug-In Hybrid Electric Vehicle Fuel Economy Reporting Methods PHEVs differ significantly from existing vehicles. They consume two fuels (petroleum and electricity) at rates that depend on the distance driven and the aggressiveness of the cycle. The Society of Automotive Engineers J1711 Recommended Practice, created in 1999, provides the fundamentals for measuring fuel economy of off-vehicle charge-capable vehicles (i.e., plug-in hybrids and electric vehicles). Seven years later, with a much better understanding of how PHEVs will likely operate, some improvements to the original procedure are recommended. The team’s specific recommendations are:

• Report gasoline and electricity consumption separately, which allows the reported results to be used for vehicle operating cost comparisons, fuel consumption, and CO2 emissions estimates. • Revise the end-of-test criteria to more accurately determine the distance driven in charge- depleting mode, fully capture the petroleum displacement potential of longer-range PHEVs, and improve the reporting accuracy for short-range PHEVs.

1 • Assume that vehicles will be fully charged once per day because there is an economic incentive for consumers to recharge their vehicles at least once per day, if not more often.

The recommended improvements to the fuel economy reporting methods have been adopted in our analyses, and the team intends to work with other labs and regulatory agencies to enact similar improvements in their procedures. The analysis also identified the need to develop a new utility factor relationship, based on the best available travel survey data, and to explore the implementation of a driving-type specific utility factor to account for the fact that most short-distance travel will be urban in nature and most long-distance travel will be highway in nature. Finally, it has been determined that the current Environmental Protection Agency certification cycle adjustment factors provide an inaccurate prediction of real-world PHEV consumption and should also be revised.

For a more detailed discussion of this effort, please refer to sections 1.1 and 1.2.

Plug-In Hybrid Electric Vehicle Cost and Consumption Benefit Analysis “Comparing the Benefits and Impacts of Hybrid Electric Vehicle Options,” a report published by the Electric Power Research Institute in July 2001, was a unique study that stands as a comprehensive analysis of hybrid electric vehicle and PHEV technology implementation. NREL contributed vehicle system simulation results to this report. The analysis scope was limited to just a few vehicle scenarios, including conventional, HEV0, PHEV20, and PHEV60. These vehicles were designed to achieve all- electric operation on the urban cycle for the specified distance.

NREL recently developed a rapid design exploration methodology and applied the methodology to an expanded PHEV analysis spectrum that includes PHEVs with a wide range of power and energy capabilities. In particular, the scope included PHEVs with limited all-electric capabilities that are still able to realize tangible petroleum displacement by operating in a “blended” charge-depleting mode.

Key conclusions from the analyses are:

• The PHEVx definition should be based on the energy equivalent all-electric range of the energy storage system, rather than on actual all-electric range (the distance before first engine turn-on event). The current all-electric range focused definition constrains the PHEV design space and is not necessarily directly related to petroleum displacement. • The expected petroleum reduction of a PHEV is substantial, but the incremental costs may present a barrier to broad market penetration. A PHEV20 would likely reduce petroleum consumption by 50% but cost $8,000 more than a conventional vehicle. The PHEV40 would cost $11,000 more than a conventional vehicle and may reduce petroleum consumption by 62%. • Data in open literature support an inverse correlation between battery cycle life and cycle depth of discharge. To provide equivalent cycle life performance, the usable state-of-charge window of a short-range PHEV must be significantly less than that of a long-range PHEV. For example, for a 15-year life, a PHEV10 can only use 41% of the capacity, while a PHEV60 may use up to 73% and still achieve battery life targets. • If PHEVs are to provide a payback relative to a hybrid electric vehicle within 10 years, based on fuel cost savings and purchase cost alone, battery costs must reach long-term, high-volume cost estimates (<$300/kWh), and gasoline costs must increase to more than $4/gal. In the absence of both lower battery costs and higher gas prices, alternative value propositions (e.g., government incentives, vehicle-to-grid revenue, battery leases, the value of a “green” image, avoided trips to the gas station, and the feel of electric operation) must be considered to overcome the cost premium of PHEVs.

2 The analysis thus far has not allowed vehicle platform engineering as a strategy to reduce costs and improve fuel economy. Aerodynamics and vehicle light-weighting will likely have a more pronounced impact on PHEVs than any other configuration. Future analysis will focus on vehicle platform enhancements and their impact on the relative costs and benefits of PHEVs. In addition, design options and alternative business models will be explored to address the high cost of batteries for PHEVs.

For more extensive discussion of this topic, please refer to sections 2.1, 2.2, 2.3, 2.4, and 2.5.

Plug-In Hybrid Electric Vehicle Real-World Performance Expectations The consumption of electricity and petroleum by a PHEV will be strongly influenced by the daily distance traveled between recharge events and the aggressiveness of driving. Rather than rely on standard test profiles for a prediction of PHEV fuel consumption, we have collaborated with municipalities to use existing drive cycle databases as inputs to our simulation models. The simulation results provide key insights into consumer travel behavior and quantify the real-world potential for PHEVs to displace petroleum. The first data set was from the St. Louis, Missouri, metropolitan area and includes 227 unique driving profiles, with daily travel distances ranging from less than a mile to more than 270 miles.

Conclusions from the travel survey data are:

• Approximately 50% of the vehicles traveled less than 29 miles a day. A PHEV with 20–30 miles of electric range capability provides sufficient energy to displace a large percentage of daily petroleum consumption. Because many vehicles drive less than 30 miles a day, the battery of a PHEV with 30 or more miles of electric range capability would likely be under- utilized on a daily basis. • The travel survey data demonstrated that there is a broad spectrum of driving behavior, varying from short to long distances and from mild to aggressive driving intensities. The Urban Dynamometer Driving Schedule and Highway Fuel Economy Test driving profiles used for fuel economy reporting today fall short of capturing the typical driving behavior of today’s consumer. • Contrary to experience with hybrid electric vehicles, which typically deliver fuel economies significantly less than their rated values, simulations of real-world driving suggest that a large percentage of drivers of PHEVs will likely observe fuel economies in excess of the rated fuel economy values. However, because of high power requirements in real-world cycles, drivers are unlikely to experience significant all-electric operation if PHEVs are designed for all- electric range on the Urban Dynamometer Driving Schedule. • If all vehicles in the travel survey “fleet” were PHEV20 vehicles designed for all-electric range on the Urban Dynamometer Driving Schedule, petroleum consumption would be reduced by 56% relative to a conventional vehicle fleet. The PHEV40 reduced consumption by an additional 12% and was equivalent to ≈1 gal/vehicle/day of petroleum savings. Including electricity costs, the average annual fuel costs savings for the “fleet” of PHEVs is more than $500/vehicle/year. • The time-of-day usage pattern obtained from global positioning system (GPS) travel survey data and the recharge requirements from simulation will be extremely valuable for determining the impact of PHEV recharging scenarios on the electric utility grid.

3 Since the St. Louis analyses were completed, data from five other metropolitan GPS travel surveys have been obtained. The driving profile database will expand from 227 to more than 2,000 vehicles. Additional analyses will be completed using the full collection of more than 2,000 driving profiles. Real-world travel simulations will be executed to consider variations in platform, aerodynamics, performance, control, and recharge scenarios. In addition, the database will be used to explore the emissions control implications of potential engine cold-starts and the fuel consumption impacts of location-specific air conditioning use.

For more extensive discussion of this topic, please refer to sections 3.1, 3.2, and 3.3.

Plug-In Hybrid Electric Vehicle Energy Management Strategies Discussion in many PHEV forums has focused on how the PHEV will function and, more specifically, on how the vehicle will use the battery and engine in combination to improve efficiency and displace petroleum.

NREL’s vehicle systems analysis team has a long history of applying optimization tools to explore hybrid electric vehicle energy management strategies. During the past fiscal year, two parallel efforts were initiated. The first explored the extensive PHEV design space and identify promising regions (using the modeling techniques developed for the cost-benefit study). The second applied dynamic programming techniques to determine the “near optimal” power distribution among the engine, motor, and battery in a PHEV for a known driving profile. NREL’s energy management strategy work is critical for maximizing the petroleum savings while protecting the batteries of future hybrid vehicles.

The conclusions from these analyses are:

• The misconception that a PHEVx must drive using electricity for the first x miles and then use the engine for the remaining travel must be clarified. This is one strategy that a manufacturer may choose to pursue, but it is not the only strategy. As long as the strategy is achieving a net discharge of the battery, petroleum will be displaced, regardless of whether the vehicle is operated on battery only or on a combination of battery and engine power (known as a “blended” control strategy). • The selection of strategy and component sizing are not entirely independent. Reducing the rated power and size of the electric traction components is one way to reduce the cost of a PHEV. Reducing electric components also necessitates the use of a “blended” strategy. The “blended” strategy can still utilize electric propulsion to the maximum extent possible to minimize the vehicle’s instantaneous fuel use. NREL’s analysis shows that a PHEV with electric traction components half the size (based on power) of an all-electric PHEV can provide nearly the same petroleum reduction as an all-electric PHEV. • Dynamic programming optimization of PHEV energy management strategies indicated that optimum control based on a priori knowledge of the driving cycle provided marginally better petroleum savings than a strategy that used stored electric energy to the greatest extent possible. On the other hand, if the real-world driving distance turned out to be less than that predicted for dynamic programming, then the “optimally” blended strategy would consume significantly more fuel than the electric energy-focused strategy over the length of the shortened driving distance. Note, however, that the simulations supporting these results were limited to repetitions of identical drive cycles. It is possible that drive cycle variation (e.g., an urban followed by a highway followed by an urban pattern) may impact this conclusion.

4 As PHEV technology evolves, energy management strategy will become increasingly important. It will be used to ensure satisfactory battery life, maximize petroleum displacement, gain performance improvement, and manage vehicle thermal and emissions transients. NREL’s future work will apply optimization to more varied driving scenarios and include aspects beyond fuel displacement in the objective function.

For more extensive discussion of this topic, please refer to sections 4.1, 4.2, and 4.3.

Summary NREL’s assessment of PHEV technology has added to the body of knowledge and continues the Vehicle Systems Analysis team’s long history of timely, innovative, objective, and quality contributions to advanced vehicle technology development. The President’s Advanced Energy Initiative defines the goal of developing a plug-in hybrid vehicle with 40 miles of electric range as a means of changing the way we fuel our vehicles. The PHEV research completed in FY06 explored this and many other potential PHEV design scenarios. PHEV technology has great potential to transition our nation’s transportation energy demand away from petroleum. However, finding ways to address the high component costs and narrow the gap between vehicle design and consumer behavior through technology optimization will be critical to achieving the petroleum displacement potential of PHEVs.

NREL will execute a continuation of its PHEV research in FY07. The goal will be to develop and demonstrate potential solutions to technical barriers identified by past research. Emphasis will be placed on fuel economy and emissions test procedures and reporting methods, real-world travel behavior analysis, exploration of alternative economic scenarios, and engine and emissions control system modeling for PHEV duty cycles. These tasks will contribute to the overall FreedomCAR PHEV research plan in the areas of analysis, research and development, and test and validation. Finally, the team plans to continue strengthening its collaborative relationships with industry colleagues. With NREL’s contributions and the contributions of others, the auto industry and the U.S. Department of Energy can lead to way toward widespread introduction of PHEV technology.

5 Section 1

For a more detailed discussion of this effort, please refer to sections 1.1 and 1.2.

6 Section 1.1

Title: “Measuring and Reporting Fuel Economy of Plug-In Hybrid Vehicles” Type: Presentation Authors: Jeff Gonder and Andrew Simpson Date: Feb. 1, 2006 Conference or Meeting: Presented to the Vehicle Systems Analysis Technical Team Abstract: Identifies modifications to the SAE J1711 Recommended Practice to better represent the fuel economy and electric consumption of PHEVs

7 Measuring and Reporting Fuel Economy of Plug-in Hybrid Electric Vehicles Jeff Gonder Andrew Simpson, Tony Markel, Aaron Brooker and Michael O’Keefe National Renewable Energy Laboratory February 1, 2006

Challenges of PHEV Fuel Economy Reporting

• Two fuel sources • Relative consumption of each fuel depends on duty cycle – distance driven between recharge events – urban, highway, high-speed • Must enable stakeholders to make comparisons with other vehicles

8 Stakeholder Perspectives

• Motorists – operating cost; comparison shopping • Policymakers/Government – national petroleum impact • Environmentalists

– national CO2 production • Manufacturers – benchmarking; certification procedures

Misleading PHEV Fuel Economy Reporting Non-Standardized Testing of Prototype Vehicle

• Cannot compare performance with other vehicles – drive cycles not standardized – driving distance not weighted

E.g. – EnergyCS reports for EDrive Prius* – 50-60 miles “boosted driving” at 100-150 mpg – “200 mpg possible…most users will get 100 mpg” – “7200 Wh usable battery window” – no charging Æ ~50 mpg (normal Prius)

4 * http://www.energycs.com/Edrive-FAQ.html

9 Recommendations

• Use modified SAE J1711 Recommended Practice • Report gasoline & electricity consumption separately – gasoline equivalency does not support range of stakeholder perspectives • Emphasize annual cost for comparison – current approach on FuelEconomy.gov

SAE J1711

Recommended Practice for Measuring Fuel Economy of Hybrid Electric Vehicles

10 Overview

• Vehicle Classifications • Operating Modes – Off Vehicle Charge –Hybrid Capable (OVC-capable) – Conventional – Not Off Vehicle Charge –EV Capable • Driving Profiles • Test Types – UDDS – Partial Charge Test – Highway – Full Charge Test –US06

Final –SC03 Both OVC-capable and not OVC-capable PCT FCT-UF Only OVC-capable vehicles vehicles PCT FCT

PCT-HEV PCT-CV FCT-EV FCT-HEV 7

“Partial Charge Test” (PCT) Charge Sustaining (CS) Operation

100%

SOC (%) Charge Sustaining SOC Level Distance

D = Two UDDS or Two HWFET Cycles

D mpg = CS gasoline 8

11 “Full Charge Test” (FCT) Charge Depleting (CD) Operation

Engine Turns On 100% Measure Recharge SOC All-Electric Range (AER) At End (%) Continued CD Operation Charge Sustaining Distance SOC Level

D = Four UDDS or Three HWFET Cycles (~30 miles) (if no engine on in first 30 miles continue to run cycles until it does turn on) D mpg = CD elec _ energy gasoline + 9 33.44kWh / gal

Weighting with National Driving Statistics

100%

90% 1995 NPTS Daily Driving Probability UF (Miles Traveled Probability) 80% “typical” daily driving (median) = 30 miles 70%

(%) “average” daily driving (mean): D 60% ∞ (P *i) 50% ∑ i = 50 miles i=0 40% 50% of fleet VMT occurs within the first 40 obability <= r 30% D ∞ P miles of travel ∑(Pi *i) + ∑(Pi * D) 20% i=0i=(D+1) UF(D) = ∞ 10% ∑(Pi *i) i=0 0% 0 100 200 300 400 500 Distance, D (mi) 10 Use Utility Factor (UF) Miles Weighting

12 Utility Factor Applied to FCT Fuel Economy

1.0

0.9 0.8 42% of fleet VMT 0.7 F U

, occurs within the r 0.6 o t c 0.5 For 30 mile FCT, first 30 miles of Fa y

it 0.4 il

t UF = 0.42 travel U 0.3

0.2

0.1

0.0 0 100 200 300 400 500 Distance, D (mi) 1 mpg = CD,UF UF (1−UF) +

11 mpgCD mpgCS

Final Steps

• Charging frequency assumption – once every two days 2 mpg = cycle 1 1 + mpgCD,UF mpgCS • Composite fuel economy – no discussion in SAE J1711 – assume retain 55/45% weighted harmonic average for city/highway

13 Important Points from SAE J1711

• Fuel consumption measured in all modes – CD fuel consumption alone overstates mpg – includes accounting of electricity usage • Utility Factor – properly accounts for and weights all trip lengths – necessary for extrapolation to national benefits

Recommended Improvements to SAE J1711

14 Improvement 1 Report Electricity Separately

• Problem: Energy Equivalence Method does not satisfy stakeholder perspectives • Solution: Report mpg, Wh/mi & annual operating cost

Example PHEVs* PHEV5 PHEV30

PCT Results 50 mpg 50 mpg

FCT Results 30 mi, 0.5 gal, 1.2 kWh 30 mi, 0.15 gal, 5 kWh

J1711 51.1 mpg, $631/yr 55.9 mpg, $577/yr

51.8 mpg, 8.4 Wh/mi, 59.3 mpg, 35.0 Wh/mi, J1711 rev. 1 $634/yr $591/yr

15 * Assumptions: Gas = $2.15/gal; Electricity = $0.09/kWh; Annual Miles = 15,000 (per EPA annual cost calculation)

Improvement 2 Determination of UF Weighting Distance

E.g. –PHEV5 and PHEV30 UDDS Scenarios:

Proposed Current Proposed 100% For PHEV5: recharge energy accurate but D is too long Engine For PHEV30: recharge On energy is low and D is too SOC short (%) Charge Sustaining SOC Level Charge Sustaining SOC Level Engine On

Cycle 1 Cycle 2 Cycle 3 Cycle 4 Cycle 5 Cycle 6 Cycle 7

D Distance D D 16

15 Improvement 2 Determination of UF Weighting Distance

•Problem:CD distance not accurately captured • Solution: End FCT test at the end of the cycle in which CS SOC is observed – most significant for PHEVs with larger batteries

Example PHEVs* PHEV5 PHEV30

PCT Results 50 mpg 50 mpg

FCT Results 30 mi, 0.5 gal, 1.2 kWh 30 mi, 0.15 gal, 5 kWh

Revised FCT 15 mi, 0.2 gal, 1.2 kWh 52.5 mi, 0.3 gal, 7.2 kWh

51.8 mpg, 8.4 Wh/mi, 59.3 mpg, 35.0 Wh/mi, J1711 rev. 1 $634/yr $591/yr 52.1 mpg, 9.6 Wh/mi, 63.3 mpg, 40.5 Wh/mi, J1711 rev. 1+2 $632/yr $564/yr

17 * Assumptions: Gas = $2.15/gal; Electricity = $0.09/kWh; Annual Miles = 15,000 (per EPA annual cost calculation)

Improvement 3 Change Charging Frequency Assumption to Once/Day

• Problem: Assumes recharge every other day • Solution: Assume charge daily – economic incentive to charge – could charge multiple times per day

Example PHEVs* PHEV5 PHEV30

PCT Results 50 mpg 50 mpg

Revised FCT 15 mi, 0.2 gal, 1.2 kWh 52.5 mi, 0.3 gal, 7.2 kWh

52.1 mpg, 9.6 Wh/mi, 63.3 mpg, 40.5 Wh/mi, J1711 rev 1+2 $632/yr $564/yr 54.3 mpg, 19.2 Wh/mi, 86.4 mpg, 80.9 Wh/mi, J1711 rev. 1+2+3 $619/yr $482/yr

18 * Assumptions: Gas = $2.15/gal; Electricity = $0.09/kWh; Annual Miles = 15,000 (per EPA annual cost calculation)

16 Open Issue: Driving Type by Daily Distance

• Currently same procedure used for both City & Highway tests, but… – CD operation likely to include more short city trips – CS operation likely to include more long highway trips • Need additional national data – for now, keep same approach for each – use future data to adjust procedures

Conclusions

• Current SAE J1711 recommended practice provides guideline for consistent reporting of hybrid vehicle fuel economy for a range of vehicle types • Revisions needed to, – fully satisfy the needs of stakeholders – more accurately report PHEV performance

17 Summary of Improvements to J1711

Example PHEVs* PHEV5 PHEV30

J1711 Original 51.1 mpg, $631/yr 55.9 mpg, $577/yr Result

Keep electricity 51.8 mpg, 8.4 Wh/mi, $634/yr 59.3 mpg, 35.0 Wh/mi, $591/yr separate

Capture all CD 52.1 mpg, 9.6 Wh/mi, $632/yr 63.3 mpg, 40.5 Wh/mi, $564/yr operation

Daily Charging 54.3 mpg, 19.2 Wh/mi, $619/yr 86.4 mpg, 80.9 Wh/mi, $482/yr (New Final Result)

In DOE and Industry’s best interest to provide useable fuel economy values

21 * Assumptions: mpgCS = 50 mpg; Gas = $2.15/gal; Electricity = $0.09/kWh; Annual Miles = 15,000 (per EPA annual cost calculation)

Next Steps

• Revive SAE J1711 Working Group – NREL provided analysis to initial recommended practice development – ANL was liaison between industry working group and EPA and CARB • Share recommendations with EPA and others

18 Section 1.2

Title: “Measuring and Reporting Fuel Economy of Plug-In Hybrid Electric Vehicles” Type: Paper Authors: Jeff Gonder and Andrew Simpson Date: October 2006 Conference or Meeting: Published at the 22nd International Battery, Hybrid, and Fuel Cell Electric Vehicle Symposium and Exposition Abstract: Identifies modifications to the SAE J1711 Recommended Practice to better represent the fuel economy and electric consumption of PHEVs

19 MEASURING AND REPORTING FUEL ECONOMY OF PLUG-IN HYBRID ELECTRIC VEHICLES1

JEFFREY GONDER National Renewable Energy Laboratory (NREL)

ANDREW SIMPSON National Renewable Energy Laboratory (NREL)

Abstract

Plug-in hybrid-electric vehicles (PHEVs) have recently emerged as a promising alternative technology to dramatically reduce fleet petroleum consumption. However, the fuel economy of many recent prototype and theoretical vehicles has varied widely and often been exaggerated in the press. PHEVs present a significant challenge as compared with conventional vehicle fuel economy reporting because they receive energy from two distinct sources and exhibit widely varying per-mile consumption, based on the drive cycle and distance driven. This paper reviews various techniques used to characterize PHEV fuel economy and discusses the relative merits, limitations, and best uses of each. This review will include a discussion of the SAE J1711 Recommended Practice issued in 1999 and will comment on how recent analysis indicates that the described procedures could be improved for reporting PHEV fuel economy. The paper highlights several critical reporting practices accurately captured by SAE J1711: use of standardized drive cycles; inclusion of charge depleting and charge sustaining operation; and using utility-factor weighting to properly combine the vehicle’s operating modes using representative driving statistics. Several recommended improvements to J1711 are also discussed: separate reporting of fuel and electricity use; better determination of the vehicle’s charge depleting performance; and application of a once-per-day vehicle charging assumption. As the U.S. Environmental Protection Agency (EPA) considers changes to window-sticker fuel economy test procedures, and the original issuance of SAE J1711 expires, the authors hope to stimulate the necessary discussion and contribute to adoption of consensus reporting metrics. In order for the resulting metrics to be useful, stakeholders must be able to translate the numbers into sound predictions of relative vehicle energy cost, petroleum use, and potential carbon dioxide (CO2) production.

Keywords: Plug-in Hybrid; Grid-connected HEVs; Vehicle Performance; Energy Efficiency, Energy Consumption; Codes, Standards, Legislation, Regulations; Environmental Impact

1 Introduction

A PHEV is a hybrid-electric vehicle (HEV) with the ability to recharge its electrochemical energy storage with electricity from an off-board source (such as the electric utility grid). The vehicle can then drive in a charge-depleting mode that reduces the system’s state-of-charge (SOC), thereby using

20 electricity to displace petroleum fuel that would otherwise be consumed. PHEVs typically have batteries that are larger than those in HEVs so as to increase the potential for petroleum displacement.

Plug-in hybrid-electric vehicles have recently emerged as a promising alternative to displace a significant fraction of vehicle petroleum consumption with electricity. This potential derives from several factors. First, PHEVs are potentially well-matched to motorists’ driving habits, particularly the distribution of miles traveled each day. Second, PHEVs can build off the success of production HEVs in the marketplace. Finally, PHEVs are very marketable in that they combine the beneficial attributes of HEVs and pure battery electric vehicles (BEVs) while simultaneously alleviating the disadvantages of each. As a result, PHEVs have the potential to come to market, penetrate the fleet, and achieve meaningful petroleum displacement relatively quickly. Few competing technologies offer this potential combined rate and timing of reduction in fleet petroleum consumption [1].

Plug-in hybrid-electric vehicles are typically characterized by a “PHEVx” notation, where “x” generally denotes the vehicle’s All-Electric Range (AER) – defined as the distance in miles that a fully charged PHEV can drive on stored electricity before needing to operate its engine. The California Air Resources Board (CARB) uses the standard Urban Dynamometer Driving Schedule (UDDS) to measure the all-electric capability of PHEVs and provide a fair comparison between vehicles [2]. According to this definition, a PHEV20 can drive 20 all-electric miles (32 kilometers) on the test cycle before the first engine turn-on. However, this all-electric definition fails to account for PHEVs that might continue to operate in charge-depleting mode after the first engine turn-on.

To better capture the range of PHEV control strategies and configurations, the authors of this paper use a different definition of PHEVx that is more-appropriately related to petroleum displacement. Under this definition, a PHEV20 contains enough useable energy storage in its battery to displace 20 miles of petroleum consumption on the standard test cycle. Note that this definition is not meant to imply all-electric capability because the vehicle operation will ultimately be determined by component power ratings, the vehicle’s control strategy, and the nature of the actual in-use driving cycle.

The key limitation of the PHEVx designation is that it is a relative metric that only describes potential petroleum displacement relative to the same vehicle operating in charge-sustaining mode. It does not provide information about absolute vehicle fuel economy. For example, a PHEV20 sedan may achieve 40 miles per gallon (mpg), or 5.9 liters per 100 kilometers (L/100 km) in charge-sustaining operation, whereas a PHEV20 SUV may only achieve 25 mpg (9.4 L/100 km), but this is not captured by the PHEVx metric. Furthermore, a fully-charged all-electric PHEV20 uses no petroleum over a 20- mile trip, leading to the impressive result of infinite miles-per-gallon (0 L/100 km) of petroleum use. Such a result is clearly helpful in marketing PHEVs, but does not provide much information about real-world potential because in reality motorists drive a variety of distances – some short, some long. An objective method is clearly needed for evaluating and reporting PHEV fuel economy, so as to avoid exaggerated claims and generate a vehicle rating that translates in some way to expectations for the real-world vehicle performance.

The reader should note that this paper will emphasize imperial units (miles and gallons for driving distance and gasoline usage, respectively) and fuel economy rather than consumption to be consistent with U.S. Government regulatory standards. Also note that, although this paper was written primarily from a fuel economy perspective (with little discussion of emissions measurement), these recommended procedures for PHEV testing and reporting are designed for suitable application to both fuel economy and emissions measurements.

2 PHEV fuel economy reporting methods

Determining a “fuel economy rating” for PHEVs presents a particular challenge as compared with other vehicle technologies because the motive power for the vehicle is derived from two distinct sources: a chemical fuel (typically gasoline) and electricity. The relative consumption of each fuel depends greatly on the duty cycle over which the PHEV operates. As with other vehicles, the type of 21 driving (urban, highway, high speed, etc.) is a very important factor, but more important to PHEVs is the distance driven between vehicle recharging events. In addition to appreciating the factors influencing fuel vs. electricity consumption, the presence of two energy sources presents a challenge in providing a rating comparable to vehicles using a single mile-per-gallon economy or liter-per-100 kilometers consumption value.

One approach would be to report only the fuel use of the vehicle. This method captures the petroleum consumption impact, but fails to account for the impacts and costs of the additional electricity consumption. Alternatively, the fuel and electricity use can be combined into a single metric that makes assumptions about the equivalent values of the two energy forms. One example is the commonly used energy-equivalency of gasoline and electricity (1 gallon [gal] = 33.44 kilowatt-hours [kWh]), which leads to a metric that accounts for both, but fails to account for differences in the supply-chain efficiency of each. Even if a different energy-equivalence factor is used to account for supply-chain efficiencies, it does not account for likely differences in the primary energy source for each supply chain. One megajoule of coal (for electricity) may have the same primary energy content as one megajoule of crude oil (for gasoline), but these sources are certainly quite different from an economic, environmental, and geopolitical perspective. Other examples of equivalency factors include cost-equivalency factors (e.g., 1 gal @ $3/gal = 30 kWh @ $0.1/kWh) and CO2 emissions-equivalency factors. However, all metrics based on equivalency factors suffer the disadvantage of not providing useful information about net petroleum consumption impact.

Ultimately, there are a variety of stakeholder perspectives that must be addressed when devising a method for fuel economy reporting. Motorists may be primarily concerned with vehicle operating costs and therefore may want a metric that conveys the magnitude of those costs. On the other hand, policymakers and environmentalists may be primarily concerned with national petroleum impact and CO2 production levels and may want a metric that can be extrapolated to the fleet level. Vehicle manufacturers, however, are obliged to focus on benchmarking and certification procedures and will also want a metric that is well-suited to this purpose.

The authors argue that the measurement technique ultimately selected must capture specific standardized performance aspects to accurately evaluate the tested vehicle with respect to annual operating costs, national petroleum impact, and CO2 production. Furthermore, the testing to obtain the performance ratings must be conducted over consistent and representative standardized driving profiles, with appropriate weightings applied to account for typical driving distances and to make comparisons with other vehicle technologies possible.

3 SAE J1711 Recommended Practice

While the various reporting approaches discussed in the previous section have been used by a variety of individuals for particular applications or analyses, the most formalized PHEV reporting procedure to date appears to be contained within the Society of Automotive Engineers (SAE) J1711 Recommended Practice for Measuring the Exhaust Emissions and Fuel Economy of Hybrid-Electric Vehicles [3]. Originally issued in 1999, the document seeks to provide a technical foundation for reporting procedures applied to a range of HEV designs, including those with “Off-Vehicle-Charge” (OVC) capability (i.e., PHEVs). Figure 1 presents a general overview of the steps in SAE J1711 that build to determining a final fuel economy rating over a particular test cycle. The specific test cycles addressed in the document include the UDDS and the Highway Fuel Economy Test (HWFET), which the EPA use for light-duty fuel economy testing.

Non-OVC-capable conventional HEVs would only complete the steps on the left side of Figure 1, whereas PHEVs follow the steps from both sides of the figure. The Partial-Charge Test (PCT) is designed to measure the vehicle’s performance in a charge-neutral hybrid operating mode, such as after a PHEV has depleted its energy storage system (ESS) to the desired charge-sustaining operating level. The Full-Charge Test (FCT) measures the vehicle’s performance when the initially fully- charged ESS is permitted a net discharge through the course of the test cycle. The bottom row in 22 Figure 1 indicates the provisions in J1711 to account for user-selectable Conventional Vehicle (CV) and Electric Vehicle (EV) operating modes. However, the test procedure discussion in this paper assumes that the PHEV is only operated in a default/hybrid operating mode. The remaining rows in the figure follow the steps through measuring the results of the PCT and FCT, applying a Utility Factor (UF) weighting to the FCT results, and then combining together the PCT and the weighted FCT results by making an assumption about how frequently the vehicle will be recharged. The remainder of this section will briefly describe each of these steps.

Final CV – Conventional Vehicle mode EV – Electric Vehicle mode PCT FCT-UF HEV – Hybrid Vehicle mode FCT – Full-Charge Test PCT FCT PCT – Partial-Charge Test UF – Utility Factor weighted PCT-HEV PCT-CV FCT-EV FCT-HEV

Figure 1: Overview of J1711 approach for determining “final” PHEV fuel economy for a test cycle based on Partial-Charge Test (PCT) and Full-Charge Test (FCT) results

Figure 2 illustrates an example of how the ESS SOC may vary over the course of the PCT. While the instantaneous SOC may move up and down during the test, the final SOC should return to roughly the same level as the initial SOC at the start of the test. The PCT fuel economy is calculated by the following equation, where “D” is the test distance in miles, “Vfuel” is the volume of fuel consumed in gallons, and “mpgCS” is taken to be the charge-sustaining mile-per-gallon rating. D mpgCS = V fuel

100%

SOC (%) Charge Sustaining SOC Level Distance

D = Two UDDS or Two HWFET Cycles Figure 2: PCT to measure Charge-Sustaining (CS) vehicle fuel economy; illustrated with application to the UDDS or HWFET test cycles Figure 3 provides a similar example of how SOC may vary over the course of the SAE J1711 FCT. The SOC begins the cycle at 100% and decreases as the vehicle is driven electrically. The distance traveled up until the PHEV engine turns on is recorded as the vehicle’s All-Electric Range (as defined in the introduction to this paper) for the particular test cycle. Following this initial engine turn-on, the vehicle may continue operating in a Charge-Depleting (CD) mode with the engine and ESS/motor working together in a blended manner to propel the vehicle. For the two principal test cycles, the FCT is terminated after four repetitions of the UDDS or three repetitions of the HWFET. However, if the engine has not turned on at that point, the cycles continue repeating until it does turn on. At the conclusion of the test, the ESS is fully recharged using off-board electricity, and the required electrical charging energy is recorded. The following equation is used to calculate the CD mile-per-gallon rating, “mpgCD,” as determined by the SAE J1711 FCT. The new terms in this equation are “Echarge,” the required electrical recharge energy in kilowatt-hours, and “Egasoline,” a constant equal to 33.44 kWh/gal

23 representing the energy content of a gallon of gasoline. Note that this approach converts the electrical recharge energy into an energy-equivalent volume of gasoline to add to the actual volume of fuel consumed. D mpgCD = Ech arg e V fuel + Egasoline

Engine Turns On 100% Measure Recharge SOC All-Electric Range (AER) At End (%) Continued CD Operation Charge Sustaining Distance SOC Level

D = Four UDDS or Three HWFET Cycles (~30 miles) (if no engine on in first 30 miles continue to run cycles until it does turn on)

Figure 3: FCT to measure Charge-Depleting (CD) fuel economy, illustrated with application to the UDDS or HWFET test cycles

100%

90% 1995 NPTS Daily Driving Probability UF (Miles Traveled Probability) 80%

) “typical” daily driving (median) = 30 miles 70% (% “average” daily driving (mean):

D 60% ∞ =

< ( Pi *i) = 50 miles

ty 50% ∑

ili i = 0 b

a 40% b 50% of fleet VMT occurs within the first 40 o r 30% D ∞ P miles of travel ∑ ( Pi *i) + ∑ (Pi * D) 20% i = 0 i = ( D+1) UF ( D) = ∞ 10% ∑ ( Pi *i) i = 0 0%0% 0 100 200 300 400 500 Distance, D (mi)

Figure 4: Illustration of Utility Factor (UF) weighting with U.S. national driving statistics

The next key step in SAE J1711 is to weight the FCT result with national driving statistics. Again, because of the focus on U.S. standards, the weighting data is taken from information on U.S. driving behavior. The purpose of the weighting is to determine on aggregate how much of a vehicle’s driving is expected to occur in its CD mode vs. in its CS mode. Figure 4 demonstrates how the appropriate weighting factor is determined. The top line in the figure represents the daily driving probability distribution determined by the 1995 National Personal Transportation Survey (NPTS) conducted in the United States. For each distance, “D,” given along the x-axis, the corresponding point on the y-axis indicated by the curve is the probability that a vehicle’s total daily driving will be less than or equal to D. The point at which the NPTS probability curve crosses 50% is the median or “typical” daily driving distance of 30 miles. However, because longer trips consist of more driving miles, the average daily driving distance is greater – 50 miles as given by the top equation in Figure 4, where “i” is the mileage increment for driving statistics in steps of 1 mile and “Pi” is the probability that a vehicle will be driven i miles per day. The utility of a CD operating mode to the vehicle fleet must be calculated

24 on a miles-driven probability basis rather than a “typical vehicle” driving basis because fuel consumption is related to total driven miles, and the 50% of vehicles with daily driving distances greater than the median account for a larger portion of all driven miles. The bottom equation in Figure 4 determines utility on a miles-driven basis, including in the utility calculation all miles for vehicles with daily driving less than the CD distance, as well as the initial miles for vehicles with daily driving greater than the CD distance. The second curve shows the resulting UF calculation as a function of D. For this curve, the interpretation of the 50% probability crossing point is that 50% of fleet Vehicle Miles Traveled (VMT) occurs within the first 40 miles of daily driving.

In SAE J1711, the FCT distance used to determine mpgCD is roughly 30 miles (assuming four UDDS cycles or three HWFET cycles). The UF value corresponding to this distance is 0.42, which would be used in the following equation to calculate the UF-weighted CD mile-per-gallon rating: “mpgCD,UF.” 1 mpg = CD,UF UF (1−UF) + mpgCD mpgCS

The final step in SAE J1711 for calculating the cycle fuel economy, “mpgcycle,” for a PHEV is to assume the vehicle is equally likely to be driven in a UF-weighted CD mode as to be driven in a CS mode. This is similar to assuming that the vehicle is equally likely to be charged daily as to never be charged at all, or that the vehicle is charged on average once every 2 days. The equation below applies this equal probability assumption. 2 mpg = cycle 1 1 + mpgCD,UF mpgCS

Because the above-described approach only determines the fuel economy for specific test cycles, it is assumed that a composite PHEV fuel economy number would have to be obtained by employing the EPA’s multi-cycle weighting methodology. The current-status EPA approach would be to apply a 55/45% weighted harmonic average to the results of the city/highway test cycles.

4 Important points and recommended changes to SAE J1711

The SAE J1711 Recommended Practice addresses several of the key issues necessary for properly measuring PHEV fuel economy. In particular, the document correctly recognizes that vehicle performance must be evaluated in both CD and CS operating modes, and that both fuel and net electricity consumption must be included. To account for the utility of CD operation, SAE J1711 also correctly applies a UF approach to account for the distribution of daily driving behavior that is weighted based on daily distances driven. This step is necessary to determine a PHEV fuel economy rating that is comparable on a national benefits scale to other vehicles’ ratings (again assuming that national driving statistics were used to generate the UF curve).

There are also several aspects of SAE J1711 that the authors recommend modifying. Three of the most important changes include keeping fuel and electricity consumption separated, better determining the CD operating distance for UF weighting, and changing the charging frequency assumption from once every other day to once daily. The remainder of this section will discuss each of these recommendations in more detail and provide an example of their relative impact.

4.1 Recommendation 1: Report electricity separately

As discussed in section 2 of this paper, the energy equivalence method of treating electricity consumption as if it were gasoline does not support the needs of stakeholders that use the vehicle’s fuel economy rating. A more useful approach to that currently suggested by SAE J1711 would be to 25 present a fuel economy and electricity consumption rating for the vehicle (such as providing a watt- hour-per-mile (Wh/mi) value in addition to the mile per gallon number). When combined with a distance driven over a period of time (that is representative of the typical daily distance distribution), these two numbers would provide an estimate of the volume of fuel used and the electrical charging energy that went into the vehicle over that operating period. A stakeholder who knew a baseline vehicle’s fuel consumption and the production mix of a certain region’s electrical utility could then take these separate fuel and electrical energy values to determine petroleum and CO2 impact. For the benefit of consumers who are typically most interested in their vehicle’s total energy cost (including fuel and electricity use), this rating approach could also consider average gasoline and electricity prices along with a typical annual driving distance to estimate a representative energy cost comparable from vehicle to vehicle.

Table 1 provides an example of the impact this revision to J1711 would have on two hypothetical PHEVs. The assumptions used to generate the annual energy cost estimates for all the tables in this paper were fuel and electricity costs of $2.50/gal and $0.09/kWh, respectively, and an annual driving distance of 15,000 miles (a typical annual VMT for U.S. drivers). Note also that all of the annual cost estimates are for illustration purposes only, as they are extrapolated from hypothetical test results over one cycle only. As the results in Table 1 illustrate, this change (to report electricity separately) does not by itself produce a large change in the energy cost estimate, but it does provide more accurate and useful information about the distribution of energy use between gasoline and electricity. Table 1: Example impact of Recommendation 1 – reporting electricity separately*

Example PHEVs PHEV5 PHEV30 PCT Results 50 mpg 50 mpg FCT Results 30 mi, 0.5 gal, 1.2 kWh 30 mi, 0.15 gal, 5 kWh J1711 51.1 mpg, $733/yr 55.9 mpg, $671/yr J1711 Recommendation 1 51.8 mpg, 8.4 Wh/mi, $735/yr 59.3 mpg, 35.0 Wh/mi, $679/yr *Assumes $2.50/gal fuel, $0.09/kWh electricity and 15,000 miles/year

4.2 Recommendation 2: Determination of utility factor (UF) weighting distance

A second recommended change to the existing J1711 reporting procedure would be to improve determination of the CD operating distance for UF weighting. Figure 5 provides an example of the SOC profile during the UDDS FCT (as described in Figure 3) for the two example PHEV5 and PHEV30 vehicles in order to demonstrate how the existing procedure could be improved. For both example vehicles, the engine turns on during the first four cycle repetitions, so the existing procedure calls for ending the test after completing the fourth cycle and measuring the recharge energy required. As the figure shows for the PHEV5 vehicle, the ESS SOC drops quickly during the first half of the initial UDDS cycle, and continues to drop at a somewhat slower rate once it begins operating in a blended (engine plus ESS/motor) mode. From partway through the second cycle until the end of the fourth cycle, the PHEV5 operates in a CS mode. For the PHEV30, the ESS discharges during all- electric vehicle operation through the first three cycles, and then continues to discharge at a slower rate during the fourth cycle as the vehicle operates in a blended mode. By the end of the fourth cycle when the existing SAE J1711 approach calls for completing the test, the ESS has not yet reached its CS SOC level. By holding the FCT to the fixed length of four-cycles, the existing J1711 approach actually averages together roughly 50% CD operation and 50% CS operation to obtain the “CD rating” for the PHEV5, and it also does not credit the PHEV30 for its continued CD operation beyond the end of the fourth cycle (instead assuming the CS rating applies to all cycles after the first four).

Instead of fixing the FCT length, the authors recommend ending the FCT after completing the cycle during which the CS SOC is reached. In a practical implementation, this would mean tracking the total Ampere-hour (Ah) discharge from the vehicle ESS and calculating when the manufacturer’s CS 26 SOC level was reached, or determining when the net ESS Ah change either increases or remains within a tolerance during all or most of one cycle. (The latter approach could result in one full cycle of CS operation included at the end of the FCT, so the following steps could be adjusted accordingly in order to set the UF-weighting distance to only include cycles in which CD operation occurred.) Assuming that it could be determined when the CS operating level was reached, the end of the cycle during which this occurred would be used as the distance, D, in the UF-weighting, and the recharge energy would be measured at this point. As Figure 5 illustrates, the modified FCT would be completed after two cycles for the PHEV5 vehicle and the recharge energy would remain basically the same. For the PHEV30 vehicle, the modified FCT would be extended to seven total cycles and the recharge energy would be greater (accurately reflecting the energy required to return the vehicle from a CS SOC state to fully-charged).

Proposed, PHEV5 Existing Proposed, PHEV30 100%

For PHEV5: recharge energy accurate but D is too long Engine For PHEV30: recharge SOC On energy is low and D is too short (%) PHEV30 Charge Sustaining SOC Level PHEV5 Charge Sustaining SOC Level Engine On

Cycle 1 Cycle 2 Cycle 3 Cycle 4 Cycle 5 Cycle 6 Cycle 7

D Distance D D Figure 5: Hypothetical FCT SOC profiles for two example PHEVs over a UDDS cycle test Table 2 presents an example of the impact this change might have on estimated energy use and cost. The table compares the result of just modifying J1711 with the separate electricity reporting recommendation to the result of using J1711 with separate electricity reporting and a modified FCT to more accurately determine the UF weighting distance. The result of the change is minor for the PHEV5 vehicle, but is noticeable for the PHEV30 vehicle – producing a 5% decrease in the annual energy cost estimate. The impact of the change should be largest for vehicles with a large ESS, for which the existing procedure potentially misses many miles of continued CD operation between the end of the FCT and when the vehicle actually begins CS operation. Table 2: Example impact of Recommendation 2 – determining UF weighting distance*

Example PHEVs PHEV5 PHEV30 PCT Results 50 mpg 50 mpg Original FCT Results 30 mi, 0.5 gal, 1.2 kWh 30 mi, 0.15 gal, 5 kWh Revised FCT Results 15 mi, 0.2 gal, 1.2 kWh 52.5 mi, 0.3 gal, 7.2 kWh J1711 Recommendation 1 51.8 mpg, 8.4 Wh/mi, $735/yr 59.3 mpg, 35.0 Wh/mi, $679/yr J1711 Recommendations 1&2 52.1 mpg, 9.6 Wh/mi, $733/yr 63.3 mpg, 40.5 Wh/mi, $647/yr *Assumes $2.50/gal fuel, $0.09/kWh electricity and 15,000 miles/year Note that to ensure CS operation follows completion of the FCT, the FCT and PCT could be combined into one single procedure to first measure CD operation and then subsequent CS operation. However, the authors anticipate that comprehensive emissions measurement will still necessitate completion of a 27 cold-start PCT, and so do not suggest moving away from two separate tests. Note also that the procedure for determining the UF weighting distance implicitly assumes that the average mpg and Wh/mile values can be uniformly applied over the vehicle’s driving up to distance, “D.” In reality, the vehicle will likely consume more electricity and less fuel early on in the cycles, and will shift to consuming more fuel and less electricity as it approaches the distance, “D.” A worthwhile approach to consider for capturing this effect would be to segment the utility factor in whole-cycle increments in order to weight the fuel and electricity use over each individual cycle for determining the total representative energy use estimate. However, the authors do not recommend this more complicated approach because the uncertainty introduced through necessary estimation of the recharge energy required for each cycle could easily offset the improved accuracy over a uniform CD operation assumption. In addition, the uncertainties in the data used to generate the UF curve could be amplified and inadvertently propagated when assigning individual weightings to each incremental cycle segment distance.

4.3 Recommendation 3: Changing the charging frequency assumption

The third recommended change to SAE J1711 is fairly simple but can have a large impact on reported energy consumption and cost. As described in section 3, the current approach averages together the UF-weighted CD result (which is intended to approximate once daily charging) and the CS result (which represents no charging). Because no reliable national data exists to predict how often PHEV drivers will plug in their vehicles, the original J1711 task force selected this equal weighting between “plug-in” and “non-plug-in” operation as a placeholder for combining the effects of these two operating modes. However, in the absence of conclusive data to capture expected charging frequency for PHEVs, the authors of this paper assert that once-per-day charging (represented by the UF-weighed CD result) is a better placeholder for combining CD and CS operation. This is because in addition to charging the vehicle either zero or one time per day, the PHEV driver could charge the vehicle multiple times per day (known as “opportunity charging”) whenever parked at a home, work, or other location that had a charging outlet.

Especially during the early years of their introduction into the market, there will likely be a large price increment between a conventional or hybrid and a comparable PHEV. In order to recover some of this initial expense, there will be a large economic incentive for PHEV drivers to take advantage of the significantly lower energy cost to operate the vehicle on electricity rather than on gasoline alone. The relatively small early market penetration levels should also require fairly little utility control over vehicle charging to avoid exacerbating peak daytime electricity demand. This would permit PHEV drivers to act on the incentive to opportunity charge several times daily. Even so, until solid data sets become available to support an average charging frequency assumption greater than once daily (or between 0-1 times per day), once daily charging provides a reasonable placeholder for this frequency assumption. Because of the economic incentive to charge, especially in the initial years of PHEV adoption and test procedure application, this once per day assumption should provide a more accurate placeholder than a once every other day assumption. Table 3: Example impact of Recommendation 3 – changing the charging frequency assumption*

Example PHEVs PHEV5 PHEV30 PCT Results 50 mpg 50 mpg Revised FCT Results 15 mi, 0.2 gal, 1.2 kWh 52.5 mi, 0.3 gal, 7.2 kWh J1711 Recommendations 1&2 52.1 mpg, 9.6 Wh/mi, $733/yr 63.3 mpg, 40.5 Wh/mi, $647/yr J1711 Recommendations 1,2&3 54.3 mpg, 19.2 Wh/mi, $716/yr 86.4 mpg, 80.9 Wh/mi, $543/yr *Assumes $2.50/gal fuel, $0.09/kWh electricity and 15,000 miles/year Table 3 provides the final example results highlighting the impact of adding this third recommended change to the first two. For both example vehicles, the final change causes the reported fuel economy

28 to increase at the expense of a higher per-mile electricity consumption rating, but ultimately provides an overall reduction in the estimated annual energy cost. The observed impact is again much greater for the PHEV30 with its larger ESS – resulting in a 16% reduction in the annual energy cost estimate.

4.4 Additional discussion

There are two significant open issues not addressed in SAE J1711 that this document does not examine in detail. The first is the correlation between driving type and driving distance. The current-status UF weighting approach implicitly assumes that the daily distance distribution of the driving represented by a particular test cycle matches the average distribution given by national (U.S.) driving statistics. For instance, with the current two-cycle city and highway EPA approach, the same national driving statistics would determine the combined CD and CS weighting for the UDDS (city driving) and for the HWFET (highway driving) before merging these values into a composite rating (by applying the 55/45% weighting of city/highway driving). This set UF weighting approach for each cycle neglects the fact that shorter city trips are likely to make up a larger fraction of CD operating miles, and longer highway trips are likely to make up a larger fraction of CS operating miles.

If future travel surveys can begin to capture the variation of driving type by daily driving distance, then a unique UF curve could be selected for each cycle. In the mean time, it once again seems most appropriate to maintain application of the uniform UF curve to each cycle evaluated. The EPA’s proposed move to a five-cycle procedure [4] will present additional challenges, not the least of which is a dramatically increased burden of up to ten tests in order to complete the PCT and FCT for each cycle. An official revision to J1711 should consider the new procedure EPA officially adopts and balance decisions to improve accuracy with those to avoid excessive testing complexity and cost.

The second challenging issue that will require further examination is how to apply EPA in-use fuel economy adjustment factors to a PHEV. The EPA introduced these adjustment factors in 1984 in an effort to quantify observed reductions in real-world fuel economy below certification cycle test results due to effects such as more aggressive driving and use of accessories (especially air conditioning). This adjustment approach is still in use today, although continued overestimation of in-use fuel economy has prompted the EPA to now consider more dramatic procedure revisions. The current methodology reduces the UDDS and HWFET test results by 10 and 22 percent, respectively, to determine the city and highway fuel economy estimates. However, the same methodology cannot be used to adjust a PHEV’s UF-weighted fuel economy and electricity consumption results because the effects that the adjustment factors are supposed to represent (such as more aggressive driving) would be observed prior to performing the UF weighting of CD and CS operation. Specifically, the adjusted cycle could impact the PCT and FCT mile per gallon and watt-hour per mile results, as well as the CD distance used for UF weighting.

One possible approach to apply the EPA adjustment factors to a PHEV would be to reduce the PCT fuel economy in the same manner as would be done for a conventional vehicle, and determine the resulting increase in fuel volume consumed over a CS distance equal to the original (UF weighting) FCT distance. The UF weighting distance for the FCT would then be assumed to remain the same, with the calculated volume of fuel added into the FCT fuel economy result. An alternate approach would be to apply the adjustment factor to the PCT and FCT fuel economy and electricity consumption results, as well as to the CD distance (resulting in a reduced distance to use with the UF weighting curve). Further analysis will be required to determine the validity of either of these approaches. Fortunately, either method would maintain some applicability to the anticipated EPA procedure changes, as the EPA proposal retains a downward adjustment of measured fuel economy results to account for effects impossible to incorporate in laboratory dynamometer testing [4].

29 5 Summary and conclusions

In its present form, the SAE J1711 recommended practice provides useful guidelines for consistent reporting of hybrid vehicle fuel economy across a range of vehicle types. Through application to standard drive cycles and weighting the utility of CD PHEV operation (based on national fleet statistics), J1711 provides a more objective comparison of PHEV performance to that of conventional and HEVs than do other less formalized rating approaches. J1711 nonetheless requires some revision to fully satisfy the needs of stakeholders using the fuel economy rating, and to further improve its accuracy in reporting PHEV performance. Table 4 summarizes the example impacts for the three major recommended changes described in this paper. Table 4: Summary of example impacts for recommended changes to SAE J1711*

Example PHEVs PHEV5 PHEV30

J1711 original result 51.1 mpg, $733/yr 55.9 mpg, $671/yr

+ Keep electricity separate 51.8 mpg, 8.4 Wh/mi, $735/yr 59.3 mpg, 35.0 Wh/mi, $679/yr

+ Better capture CD distance 52.1 mpg, 9.6 Wh/mi, $733/yr 63.3 mpg, 40.5 Wh/mi, $647/yr

+ Assume once daily 54.3 mpg, 19.2 Wh/mi, $716/yr 86.4 mpg, 80.9 Wh/mi, $543/yr charging (New final result) *Assumes 50 mpg PCT, $2.50/gal fuel, $0.09/kWh electricity and 15,000 miles/year The new results for the modified reporting approach provide a more accurate estimate of the petroleum savings each of these vehicles could provide, which was understated by the original J1711 result. Specifically, the petroleum consumption estimate is reduced by 6% for the PHEV5 and by 35% for the PHEV30. The new results also provide an estimate of the electricity consumption per mile that a typical user could expect the vehicle to achieve. From this more accurate description distinguishing fuel from electricity use, and assuming once daily charging, the results demonstrate a 2% reduction in the annual energy cost estimate for the PHEV5 and a 19% reduction in the annual energy cost estimate for the PHEV30 relative to the original J1711 result. The magnitude of the improved estimates for petroleum use and energy cost are greater for longer distance rated PHEVs because of the potential offered by their larger energy storage systems.

It is in the best interest of all those evaluating the potential benefits of PHEVs to be able to objectively evaluate the technology relative to other vehicles. It should likewise be in the best interest of PHEV advocates to establish and follow consensus PHEV reporting procedures to avoid accusations of providing unfounded “hype” for the technology. In particular, the adopted procedures should characterize PHEV performance over a representative range of driving conditions, including proper weighting of typical vehicle daily driving distances. A discussion of accurate and objective PHEV fuel economy reporting is particularly important in the present context of increasing technical interest in PHEVs, expiration of the original issuance of SAE J1711 and EPA’s proposal to change the agency’s conventional vehicle test procedures. It is the authors’ hope that the issues raised in this paper help stimulate the necessary discussion and contribute to adoption of consensus reporting metrics. As discussed in this paper, for the resulting metrics to be useful, stakeholders must be able to translate the numbers into sound predictions of relative vehicle energy cost, petroleum use, and potential carbon dioxide (CO2) production.

Acknowledgement

The authors would like to acknowledge the programmatic support of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program.

30 List of symbols and acronyms

AER – all-electric range BEV – battery electric vehicle CARB – California Air Resources Board CD – charge depleting CO2 – carbon dioxide CS – charge sustaining CV – conventional vehicle D – distance [miles] DOE – U.S. Department of Energy Echarge – electrical recharge energy Egasoline – gasoline energy content (33.44 kWh/gal) ESS – energy storage system EPA – U.S. Environmental Protection Agency EV – electric vehicle FCT – Full-Charge Test HEV – hybrid electric vehicle HWFET – Highway Fuel Economy Test i – mileage increment for driving statistics mpgX – mile-per-gallon rating in mode X OVC – off-vehicle charge NPTS – National Personal Transportation Survey PCT – Partial-Charge Test PHEV – plug-in hybrid electric vehicle Pi – probability i miles driven in a day SAE – Society of Automotive Engineers SOC – state of charge (of the ESS) UDDS – Urban Dynamometer Driving Schedule UF – Utility Factor Vfuel – fuel volume consumed [gallons] VMT – vehicle miles traveled

References

[1] Markel, T., O’Keefe, M., Simpson, A., Gonder, J., and Brooker, A. Plug-in HEVs: A Near-term Option to Reduce Petroleum Consumption, Milestone Report, National Renewable Energy Laboratory, 2005. [2] California Air Resources Board, California Exhaust Emission Standards and Test Procedures for 2005 and Subsequent Model Zero-Emission Vehicles, and 2002 and Subsequent Model Hybrid Electric Vehicles, in the Passenger Car, Light-Duty Truck and Medium-Duty Vehicle Classes, California Environmental Protection Agency, 2003. [3] Society of Automotive Engineers Surface Vehicle Recommended Practice, SAE J1711 – Recommended Practice for Measuring Fuel Economy of Hybrid-Electric Vehicles, Society of Automotive Engineers Publication, Issued March 1999. [4] Environmental Protection Agency, Fuel Economy Labeling of Motor Vehicles: Revisions to Improve Calculation of Fuel Economy Estimates, 40 CFR Parts 86, 600, EPA: Notice of Proposed Rulemaking, EPA-HQ- OAR-2005-0169.

Authors Jeffrey Gonder, Research Engineer, National Renewable Energy Laboratory (NREL), 1617 Cole Blvd; Golden, CO 80401 USA; Tel: 303-275-4462; Fax: 303-275-4415; [email protected]. Jeff joined the Advanced Vehicle Systems Group at NREL in 2005. His research includes systems analysis of plug-in hybrid electric vehicles and novel hybrid control strategies. Jeff holds a Master’s degree in Mechanical Engineering from The Pennsylvania State University and a Bachelor’s degree in the same subject from the University of Colorado. Prior to joining NREL, Jeff developed fuel cell systems and vehicles at Anuvu Inc. in Sacramento, CA. In graduate school, Jeff researched direct methanol fuel cells and helped lead the Penn State FutureTruck hybrid vehicle competition team.

Andrew Simpson, Vehicle Systems Engineer, National Renewable Energy Laboratory (NREL), 1617 Cole Blvd, Golden CO 80401 USA; Tel: 303-275-4430; Fax: 303-275-4415; [email protected]. Andrew joined the Advanced Vehicle Systems Group at NREL in 2005 and his current focus is plug-in hybrid-electric vehicles. He holds a Bachelor of Mechanical Engineering (2000) and Ph.D. in Electrical Engineering (2005) from the University of Queensland, Brisbane, Australia. Prior to NREL, Andrew worked as a CFD consultant for Maunsell Australia. He co-founded the Sustainable Energy Research Group at The University of Queensland and was a coordinating member of the University’s “SunShark” solar racing team which competed successfully from 1996-2000.

31 Section 2

Key conclusions from the analyses are:

The analysis thus far has not allowed vehicle platform engineering as a strategy to reduce costs and improve fuel economy. Aerodynamics and vehicle light-weighting will likely have a more pronounced impact on PHEVs than any other configuration. Future analysis will focus on vehicle platform enhancements and their impact on the relative costs and benefits of PHEVs. In addition, design options and alternative business models will be explored to address the high cost of batteries for PHEVs.

For more extensive discussion of this topic, please refer to sections 2.1, 2.2, 2.3, 2.4, and 2.5.

32 Section 2.1

Title: “Cost/Benefit Analysis of Hybrid-Electric and Plug-In Hybrid-Electric Vehicle Technology” Type: Presentation Authors: Andrew Simpson Date: March 1, 2006 (updated May 2006) Conference or Meeting: Presented to the Vehicle Systems Analysis Technical Team Abstract: Explores the spectrum of PHEV design space with respect to battery options and quantifies the most cost-effective scenarios

33 Cost / Benefit Analysis of Hybrid-Electric and Plug-In Hybrid-Electric Vehicle Technology

A presentation to the FreedomCAR Vehicle Systems Analysis Technical Team by

Andrew Simpson National Renewable Energy Laboratory

Presented Wednesday, 1st March 2006 REVISED May 2006

With support from the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program

Disclaimer and Government License This work has been authored by Midwest Research Institute (MRI) under Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy (the “DOE”). The United States Government (the “Government”) retains and the publisher, by accepting the work for publication, acknowledges that the Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work or allow others to do so, for Government purposes. Neither MRI, the DOE, the Government, nor any other agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe any privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the Government or any agency thereof. The views and opinions of the authors and/or presenters expressed herein do not necessarily state or reflect those of MRI, the DOE, the Government, or any agency thereof.

34 The Perfect Storm

Domestic Production Domestic Consumption 20

• Petroleum consumption has steadily ) ay d / increased while domestic production has b 15 m (m

continued to decline m u

e 10 l o tr • World oil production predicted to peak e P within the next 5-15 years 5 Source: U.S. Department of Energy, Energy Information Administration 0 • Recent increase in gasoline price is 1940 1950 1960 1970 1980 1990 2000 2010 indicator of growing tension between supply and demand Gasoline price - 200% rise in 8 years!

$3.50

on) $3.00 gall $/ ( e

c $2.50 i r ne P i

ol $2.00 as Source: Hubbert Center Newsletter #99/1 R. Udall and S. Andrews age G

er $1.50 v A onal i $1.00 WHAT’S OUR PLAN? Nat ly

eek $0.50 W Source: US Dept of Energy, Energy Information Administration

$- 3 1990 1993 1995 1998 2001 2004 2006

Oil Use Reduction with HEVs

Light Duty Fleet Oil Use - Impact of HEVs on Consumption

16 2005 AEO Base Case HEV Scenario 14

12 Oil use same as today! BD)

P 10 3 MBPD M on ( i

pt 8 um

6 Cons l i O 4 This highly aggressive scenario assumes 100% HEV sales from 2010 onwards… 2

0 2000 2005 2010 2015 2020 2025 2030 Year

Produced using VISION model, MBPD = million barrels per day 4

35 Key Messages

1. There is a very broad spectrum of HEV-PHEV designs. 2. Reduction in per-vehicle fuel consumption via HEVs is limited, whereas PHEVs can reduce per-vehicle fuel use much further. 3. PHEVs have higher powertrain costs than HEVs, but have lower “fuel” costs than HEVs. 4. PHEVs are the most-cost-effective choice in a scenario of projected (low) battery costs and high fuel costs.

Presentation Outline

• Study Objectives • Potential petroleum reduction from PHEVs • Simulation of PHEV efficiency and cost — Baseline vehicle assumptions — Components models (cost, mass, volume, efficiency) — Control strategy • Results — Component sizing — Fuel Economy — Incremental cost — Payback scenarios • Conclusions & Next Steps

36 Study Objective

Question: Under what circumstances do PHEVs “make sense”?

Subject to: — battery energy / PHEV range — battery power / degree of hybridization (DOH) — control strategy including SOC/DOD window — battery chemistry — fuel prices — driving habits

Potential Gasoline Savings from Plug-In HEVs

3 ways to reduce petroleum consumption by 50% 100%

90% PHEV40 PHEV20 HEV m +0% cs mpg +40% cs mpg +100% cs mpg 80% oleu ) 70% PHEV60 60%

ption (% PHEV40

m 50%

u Prius (Corolla) 40% Escape tion in Total Petr Civic Challenging for Cons 30% PHEV20 HEV technology Accord 20% Reduc Highlander Vue 10% WHAT ARE THE RELATIVE COSTS? HEV 0% 0% 20% 40% 60% 80% 100% RReduceducttioionn i nin c harge-sChargeu-Susstainitaingn (cs)ing mModeode pe Petroltroleueumm Cconsuonsummptptionion (% (%)) 8

37 PHEV Battery Options

ESS Technology Comparison - P/E Ratios

1000 500 200 100 50 20 2000

1800 SAFT VLP 7

1600

1400 SAFT VLP 20 Varta Ni UHP

) Expanded PHEV Kokam SLPB100216216 g PEVE 7.5Ah

/k 1200

W SAFT VLP 30 design space Kokam SLPB78216216 ( r Cobasys 1000 e Kokam SLPB60460330 w 1000 o SAFT VL30P module SAFT VLM 27 Kokam SLPB55205130

p Kokam SLPB41205130 ic f i Varta Li 6Ah Kokam SLPB80460330 c e 800 SAFT VLM 41 Kokam SLPB30205130 p 5

S Sprinter SAFT Cobasys 4500 SAFT VLE 45 Cyclon D SAFT VL41M module 600 Optima D51 Optima D35 Prius PEVE SAFT VLE module 400 Optima D34 Varta Ni HP SAFT VHF 30S Varta Li 60Ah SAFT VHF 20S 2 SAFT VHF 10S SAPHION U1-12FN40 Cyclon E Cobasys 9500 Prius+ SAPHION 200 SAPHION U24-12FN100 1 Sprinter Varta SAFT NiMH 12 SAPHION U27-FN130 SAFT STM 5-100 MR SAFT STM 5-140 MR Avestor SE 48S63 0 0306090120150 Available specific energy (Wh/kg) 9

Simulation of PHEV Efficiency and Cost

70000 100% Approach 60000 90% 50000 • Power-flow simulation in 80% spreadsheet 40000 70% 30000 60% • Mass-compounding produced via engine 20000 50% motor circular referencing SOC 10000 40%

• Vehicle configurations: 0 30% 0 1020304050607080

— conventional automatic -10000 20%

-20000 10%

-30000 0% — pre-transmission parallel hybrid: HEV or PHEV • Calculates component sizes and costs including 74% retail markup • Measures fuel/electricity consumption using NREL-proposed revisions to SAE J1711 (uses national average driving statistics) • 2 powertrain technology scenarios – near term and long term • Battery definition is key input to the simulation

38 Powertrain Technology Scenarios

Battery Near-Term Scenario Long-Term Scenario Chemistry NiMH Li-Ion Module cost Double EPRI projections, see slide 20 EPRI projections, see slide 20 Pack cost $ = ($/kWh + 13) x kWh + 680 (from EPRI study) Same Module mass NiMH battery design function (Delucchi), see slide 19 Li-Ion battery design function (Delucchi), see slide 19 Pack mass Tray + straps + thermal management = 0.06 kg/kg Same Harness + bus bars = 0.14 kg/kW (Delucchi)

Efficiency Equivalent circuit model based on P/E ratio Same SOC window Based on JCI data for NiMH, see slide 21 Assumes Li-Ion achieves same cycle life as NiMH

Motor Near-Term Scenario Long-Term Scenario Mass DOE 2006 current status, see slide 15 Based on GM Precept motor drive, see slide 15 Efficiency 95% peak efficiency curve, see slide 14 Same Cost From EPRI study, see slide 16 From EPRI study, see slide 16

Engine Near-Term Scenario Long-Term Scenario Mass Based on MY2003 production engines, see slide 15 Same Efficiency 35% peak efficiency curve, see slide 13 Same Cost From EPRI study, see slide 16 Same

Baseline Vehicle Characteristics

MIDSIZE SEDAN (AUTOMATIC) – MSRP $23,400

Platform Parameters

Glider Mass 905 kg

Curb Mass 1429 kg

Test Mass 1565 kg (136 kg load)

Gross Vehicle Mass (GVM) 1899 (470 kg load)

Drag coefficient 0.30

Frontal area 2.27m2

Rolling resistance coefficient 0.009

Accessory load 700 W elec. or 823 W mech.

Performance Parameters

Standing acceleration 0-60 mph in 8.0 s

Passing acceleration 40-60 mph in 5.3 s

Top speed 110 mph

Gradeability 6.5% at 55 mph at GVM with 2/3 fuel converter power

Vehicle attributes

Engine power 121 kW

Fuel economy 22.2 / 35.2 / 26.6 mpg (urban / highway / composite, unadjusted)

39 Engine Map

Engine Efficiency Map - 4 Cylinder

40%

35%

30% ) 25% (% y c

n 20% e i c

ffi 15% E 10%

0% 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Normalised output power (-)

Based on 1.9L, 95kW gasoline engine

Motor/Controller Map

Motor/Controller Efficiency Map

100% 90% 80% 70% )

% 60% y (

c drive 50% en regen ci i

f 40% f E 30% 20% 10% 0% 0.00.10.20.30.40.50.60.70.80.91.0 Normalised output power (-)

Based on 50kW permanent magnet motor/controller

40 Engine / Motor Masses

300

250 Engine Motor/Cont. (near-term) Motor/Cont. (long-term) 200 ) g k (

s 150 s Ma

100

0 0 20 40 60 80 100 120 140 Power (kW peak)

Engine / Motor Costs

$4,000

$3,500

$3,000 Engine Motor/Cont. (near-term) Motor/Cont. (long-term) $2,500 ($) t os

ed C $2,000 ur t ac

anuf $1,500 M

$1,000

$500

$- 0 20406080100120140 Power (kW peak)

41 Simulation Validation

Conventional Automatic Midsize Sedan

Unadjusted Fuel Economy

Urban Highway Composite

Benchmark 22.2 mpg 35.2 mpg 26.6 mpg vehicle < 1% error

70000

60000

50000

40000

30000

20000 Simulated 22.3 mpg 34.9 mpg 26.6 mpg

10000

0 vehicle 012345678

-10000

-20000

-30000

Battery Definition as Key Input to Simulation

Input parameters that define the battery in BLUE

mass compounding Benefit of PHEV range plugging-in

kWh/mi kWh usable (from simulation)

SOC window kWh total Total MPG Benefit

P/E ratio kWmotor Benefit of DOH Performance hybridization constraints kWengine

DOH = degree of hybridization 18

42 Battery Model based on P/E Ratio

Battery Design Functions

2000 P/E = 30 NiMH (near-term scenario) 1800 LI-ION (long-term scenario)

1600

1400 g) k / 1200 (W er

ow 1000 P c i f 800 peci S 600

400 P/E = 2 200

0 0 20 40 60 80 100 120 140 160 180 200 Specific Energy (Wh/kg)

Battery Model based on P/E Ratio

Battery Module Cost Functions

1200 Near Term Long Term 1000

) 800 h W k ($/ t s

o 600 C e l odu

M 400

200

0 0 5 10 15 20 25 30 Power-to-Energy (P/E) Ratio

43 Battery Model (cont.) – SOC Window

Battery SOC Operating Window vs. Specified All-Electric Range

100%

90%

80%

70% SOC operating window

60%

50%

40% SOC widow

30% Daily mileage distribution

20%

10%

0% 0 102030405060 Daily Mileage / All-Electric Range

HEV/PHEV Control Strategy

• Engine power request adjusted w.r.t. battery SOC

—Pengine = Pdriveline – k (SOC – SOCtarget) Idle-off for extended periods allows some PHEVs to avoid • Engine turn-on/off strategy: significant fuel-use penalty — engine turn-on occurs when during charge-depleting mode. To enable this, modifications to » SOC-adjusted engine power request > 0 emissions control system would be required e.g. — idle-off allowed when vacuum-insulated catalyst… » SOC-adjusted engine power request <= 0 However, all vehicles (conventional and hybrid) can » AND vehicle is stationary benefit from these systems » AND engine has been on for at least 5 minutes • Motor provides supplemental power and does 60% of braking regeneratively (net recapture subject to component efficiencies) • All component power limits are enforced • Electric accessories

44 Results

PHEV Design Space

Near-term cost scenario NIMH BATTERIES 100 Maximum engine downsizing HEV0

PHEV2

Increasing battery energy PHEV5 o i t a

R PHEV10 ergy n E

r/ 10 PHEV20 e w o P ry

e PHEV30 t t a B

PHEV40

PHEV50 Increasing battery power PHEV60 1 0% 10% 20% 30% 40% 50% 60% Degree of Hybridization (%)

45 Results – Fuel Consumption Reduction

Reduction in Fuel Consumption vs Degree of Hybridization Near-term cost scenario NIMH BATTERIES 100%

HEV0 90% Maximum engine downsizing PHEV2 80%

) PHEV5 % 70% on ( i Increasing battery energy pt 60% PHEV10 um ns o C

l 50% PHEV20 ue n F

i 40% PHEV30 on i t uc

d 30% e

R PHEV40

20% PHEV50

10% Increasing battery power PHEV60 0% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Degree of Hybridization (DOH)

Results – Powertrain Cost Increments

Cost Increment relative to Conventional Vehicle (Midsize Sedan) Near-term cost scenario NIMH BATTERIES $40,000

HEV0 Maximum engine downsizing $35,000 PHEV2

$30,000 PHEV5

$25,000 PHEV10 t n e m

cre $20,000 PHEV20 n i st o C $15,000 PHEV30

PHEV40 $10,000

PHEV50 $5,000

PHEV60 $- 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Degree of Hybridization (DOH)

46 Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans (based on NREL-revised SAE J1711) $35,000 Near-term cost scenario NOTE: maximum engine downsizing is a NIMH BATTERIES $30,000 result of performance constraints – less stringent constraints move the knee of the curve to the right and improve the viability of higher-DOH designs $25,000 t n e m e r $20,000 Inc t s o Increasing DOH in C a

r $15,000 t r Maximum engine e downsizing Pow $10,000

HEV0 $5,000

$- 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Reduction in Fuel Consumption (%)

Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans (based on NREL-revised SAE J1711) $35,000

Near-term cost scenario HEV0 NIMH BATTERIES $30,000 PHEV2

$25,000 PHEV5 t

emen PHEV10

cr $20,000 n I t s PHEV20 Co n i a

r $15,000 t PHEV30 wer o P

$10,000 PHEV40

PHEV50 $5,000

PHEV60 $- 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Reduction in Fuel Consumption (%) 564 gal, $1692 per year @ $3.00/gal.

47 Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans (based on NREL-revised SAE J1711) $35,000

Long-term cost scenario HEV0 LI-ION BATTERIES $30,000 PHEV2

$25,000 PHEV5 t n e m

e PHEV10 r

c $20,000 n I st

o PHEV20 C n i

ra $15,000 rt

e PHEV30 w o P

$10,000 PHEV40

PHEV50 $5,000

PHEV60 $- 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Reduction in Fuel Consumption (%) 564 gal, $1692 per year @ $3.00/gal.

Optimum PHEV Designs – Near & Long Term Scenarios

Near-term: NIMH BATTERIES Vehicle Curb Mass Engine Motor DOH Battery P/E SOC MPG Wh/mi Retail Cost (kg) (kW) (kW) (kWh) (1/h) Increment CV 1429 121.7 ------26.6 0 $ - HEV0 1453 78.3 41.0 34% 1.5 30.0 37% 35.1 0 $ 5,199 PHEV2 1453 78.3 41.0 34% 1.5 30.0 37% 36.3 11 $ 5,863 PHEV5 1493 79.7 39.3 33% 3.5 12.0 39% 37.8 28 $ 7,326 PHEV10 1566 81.9 45.0 35% 6.9 7.0 41% 40.6 54 $ 10,389 PHEV20 1666 85.0 47.0 36% 12.6 4.0 47% 47.6 101 $ 14,980 PHEV30 1747 88.2 48.5 35% 17.3 3.0 53% 55.6 138 $ 18,754 PHEV40 1820 89.9 54.2 38% 21.1 2.8 59% 64.2 171 $ 22,129 PHEV50 1875 91.6 56.6 38% 24.2 2.5 66% 74.6 197 $ 24,737 PHEV60 1913 92.8 56.2 38% 26.6 2.3 73% 84.1 218 $ 26,617

Long-term: LI-ION BATTERIES Vehicle Curb Mass Engine Motor DOH Battery P/E SOC MPG Wh/mi Retail Cost (kg) (kW) (kW) (kWh) (1/h) Increment CV 1429 121.7 ------26.6 0 $ - HEV0 1416 77.2 40.4 34% 1.4 30.0 37% 35.5 0 $ 3,261 PHEV2 1416 77.2 40.4 34% 1.4 30.0 37% 36.7 11 $ 3,925 PHEV5 1438 77.8 38.4 33% 3.4 12.0 39% 38.4 27 $ 4,623 PHEV10 1478 79.1 43.3 35% 6.6 7.0 41% 41.7 52 $ 6,147 PHEV20 1528 80.7 44.5 36% 11.9 4.0 47% 49.6 95 $ 8,314 PHEV30 1563 81.8 45.0 36% 16.1 3.0 53% 58.9 129 $ 10,002 PHEV40 1588 82.6 44.8 35% 19.2 2.5 59% 68.6 157 $ 11,236 PHEV50 1610 83.3 45.8 35% 21.8 2.3 66% 80.4 180 $ 12,311 PHEV60 1637 84.1 50.4 37% 23.9 2.3 73% 90.2 198 $ 13,406

48 Annual Vehicle Mileage Schedule used in following payback scenarios

Vehicle Mileage Schedule from NHTSA (2006)

16000 180

Annual VMT ds n a

Cumulative VMT s 14000 160 150,000 miles after 15 years ou Th 140 12000

120 10000 T M T M 100 V e V v l ti

a 8000 u la n 80 u m An u

6000 C 60

4000 40

2000 20

0 0 123456789101112131415 Year after purchase

Fuel Use Reduction from HEVs and PHEVs

Cumulative Per-Vehicle Fuel Use Near-term cost scenario NIMH BATTERIES 6000 CV HEV0 PHEV10 5000 PHEV20 PHEV40 PHEV60 138 bbl

4000 ons) l al g ed ( s

U 3000 el Fu e n i l o s

a 2000 G

1000 44 bbl

0 02468101214 Time (years)

49 Cost Reduction from HEVs and PHEVs

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs Near-term cost scenario NIMH BATTERIES $60,000

$50,000

$40,000 st o C e

v CV

ti $30,000

la HEV0 u m

u PHEV10 C PHEV20 $20,000 PHEV40 $3.00 / gal. (today) PHEV60

$10,000 $0.09¢/kWh (2005 average)

$- 02468101214 Years after purchase

Cost Reduction from HEVs and PHEVs (cont.)

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs Long-term cost scenario LI-ION BATTERIES $60,000

$50,000

$40,000 st o C e

v CV

ti $30,000

la HEV0 u m

u PHEV10 C PHEV20 $20,000 PHEV40 $3.00 / gal. (today) PHEV60

$10,000 $0.09¢/kWh (2005 average)

$- 02468101214 Years after purchase

50 Cost Reduction from HEVs and PHEVs (cont.)

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs Long-term cost scenario LI-ION BATTERIES $60,000

$50,000

$40,000 st o C e

v CV

ti $30,000

la HEV0 u m

u PHEV10 C PHEV20 $20,000 PHEV40 $4.00 / gal. (tomorrow?) PHEV60

$10,000 $0.09¢/kWh (2005 average)

$- 02468101214 Years after purchase

Cost Reduction from HEVs and PHEVs (cont.)

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs Long-term cost scenario LI-ION BATTERIES $60,000

$50,000

$40,000 st o C e

v CV

ti $30,000

la HEV0 u m

u PHEV10 C PHEV20 $20,000 PHEV40 $5.00 / gal. (day after tomorrow??) PHEV60

$10,000 $0.09¢/kWh (2005 average)

$- 02468101214 Years after purchase

51 HEV/PHEV Payback is Sensitive to Annual Mileage

Cumulative Incremental Vehicle plus Energy (Fuel/Elec.) Costs relative to conventional vehicle $12,000 Long-term cost scenario LI-ION BATTERIES $10,000 $4.00 / gal. $0.09¢/kWh st o

C $8,000

tal 5000mi per year difference varies en PHEV40 payback by 4 years em HEV0

cr $6,000 n I e

v PHEV40 ti a l u $4,000 m

u 15kmi per year

C NHTSA mileage schedule ~10kmi per year average $2,000

$- 02468101214 Years after purchase

Vehicle Costs cont.

Why might PHEV buyers pay more? 1. Tax incentives

2. Reduced petroleum use, air pollution and CO2 3. National energy security 4. Less maintenance 5. Reduced fill-ups 6. Convenience of home recharging (off-peak) 7. Improved acceleration (high torque of electric motors) 8. Green image, “feel-good factor” 9. Vehicle-to-grid (V2G)

52 Conclusions

1. There is a very broad spectrum of HEV-PHEV designs. 2. Key factors in the HEV/PHEV cost-benefit equation include: • Battery costs • Fuel costs • Control strategy (battery SOC window and emissions considerations) • Driving habits (annual VMT and trip-length distribution) 3. Based on the midsize vehicle platform, performance constraints, component technologies and control strategies used in this study: • HEVs can reduce per-vehicle fuel use by approx. 25%. Note this study did not consider benefits from platform engineering (i.e. mass/drag reduction). • PHEVs can reduce per-vehicle fuel use by up to 45% for PHEV20s, up to 60% for PHEV40s and up to 70% for PHEV60s. • In the long term, powertrain cost increments are predicted to be $2-5k for HEVs, $7-11k for PHEV20s, $10-14k for PHEV40s and $13-17k for PHEV60s assuming that projected component (battery) costs can be achieved.

Conclusions (cont.)

4. Based on combined powertrain and energy costs: • At today’s fuel and powertrain component costs, conventional vehicles are the most cost-competitive. • HEVs become the most cost-competitive EITHER if fuel prices increase OR projected battery costs are achieved. • PHEVs become cost-competitive ONLY if projected battery costs are achieved AND fuel prices increase. • Since high-range PHEVs reduce petroleum consumption the most, they might be good candidates for subsidies or tax- incentives to improve their cost-competitiveness and help realize national security goals. • Alternative business models (e.g. battery lease) could significantly improve the market for PHEVs

53 Next Steps

• Expand the HEV-PHEV analysis space to include: — Platform engineering (mass/drag reduction) — Different performance constraints / component sizes • Detailed simulation of promising PHEV designs: — Real world driving patterns (e.g. St Louis data) — Control strategy optimization

• Optimization of PHEV competitiveness using Technical Targets Tool — Various gas price and battery cost scenarios

54 Section 2.2

Title: “Cost-Benefit Analysis of Plug-In Hybrid Electric Vehicle Technology” Type: Paper Authors: Andrew Simpson Date: October 2006 Conference or Meeting: Published at the 22nd International Battery, Hybrid, and Fuel Cell Electric Vehicle Symposium and Exposition Abstract: Explores the spectrum of PHEV design space with respect to battery options and quantifies the most cost-effective scenarios

55 COST-BENEFIT ANALYSIS OF PLUG-IN HYBRID ELECTRIC VEHICLE TECHNOLOGY1

ANDREW SIMPSON National Renewable Energy Laboratory

Abstract

Plug-in hybrid-electric vehicles (PHEVs) have emerged as a promising technology that uses electricity to displace petroleum consumption in the vehicle fleet. However, there is a very broad spectrum of PHEV designs with greatly-varying costs and benefits. In particular, battery costs, fuel costs, vehicle performance attributes and driving habits greatly-influence the relative value of PHEVs. This paper presents a comparison of the costs (vehicle purchase costs and energy costs) and benefits (reduced petroleum consumption) of PHEVs relative to hybrid-electric and conventional vehicles. A detailed simulation model is used to predict petroleum reductions and costs of PHEV designs compared to a baseline midsize sedan. Two powertrain technology scenarios are considered to explore the near-term and long-term prospects of PHEVs. The analysis finds that petroleum reductions exceeding 45% per- vehicle can be achieved by PHEVs equipped with 20 mi (32 km) or more of energy storage. However, the long-term incremental costs of these vehicles are projected to exceed US$8,000, with near-term costs being significantly higher. A simple economic analysis is used to show that high petroleum prices and low battery costs are needed to make a compelling business case for PHEVs in the absence of other incentives. However, the large petroleum reduction potential of PHEVs provides strong justification for governmental support to accelerate the deployment of PHEV technology.

Keywords: Plug-in Hybrid; Hybrid-Electric Vehicles; Battery, Secondary Battery; Modeling, Simulation; Energy Security.

1 Introduction to Plug-In Hybrid-Electric Vehicles

Plug-in hybrid-electric vehicles have recently emerged as a promising alternative that uses electricity to displace a significant fraction of fleet petroleum consumption [1]. A plug-in hybrid-electric vehicle (PHEV) is a hybrid-electric vehicle (HEV) with the ability to recharge its electrochemical energy storage with electricity from an off-board source (such as the electric utility grid). The vehicle can then drive in a charge-depleting (CD) mode that reduces the system’s state-of-charge (SOC), thereby using electricity to displace liquid fuel that would otherwise have been consumed. This liquid fuel is typically petroleum (gasoline or diesel), although PHEVs can also use alternatives such as biofuels or hydrogen. PHEV batteries typically have larger capacity than those in HEVs so as to increase the potential for petroleum displacement.

1.1 Plug-In Hybrid-Electric Vehicle Terminology Plug-in hybrid-electric vehicles are characterized by a “PHEVx” notation, where “x” typically denotes the vehicle’s all-electric range (AER) – defined as the distance in miles that a fully charged PHEV can drive before needing to operate its engine. The California Air Resources Board (CARB) uses the standard Urban Dynamometer Driving Schedule (UDDS) to measure the AER of PHEVs and provide a fair comparison between vehicles [2]. By this definition, a PHEV20 can drive 20 mi (32 km) all- electrically on the test cycle before the first engine turn-on. However, this all-electric definition fails

56 to account for PHEVs that might continue to operate in CD-mode after the first engine turn-on. Therefore, the author uses a definition of PHEVx that is more appropriately related to petroleum displacement. By this definition, a PHEV20 contains enough useable energy storage in its battery to displace 20 mi (32 km) of petroleum consumption on the standard test cycle. Note that this definition does not imply all-electric capability since the vehicle operation will ultimately be determined by component power ratings and their control strategy, as well as the actual in-use driving cycle.

1.2 The Potential of Plug-In Hybrid-Electric Vehicles The potential for PHEVs to displace fleet petroleum consumption derives from several factors. First, PHEVs are potentially well-matched to motorists’ driving habits – in particular, the distribution of distances traveled each day. Based on prototypes from the last decade, PHEVs typically fall in the PHEV10-60 range [3]. Figure 1 shows the US vehicle daily mileage distribution based on data collected in the 1995 National Personal Transportation Survey (NPTS) [4]. Clearly, the majority of daily mileages are relatively short, with 50% of days being less than 30 mi (48 km). Figure 1 also shows the Utility Factor (UF) curve for the 1995 NPTS data. Daily Mileage Distribution and Utility Factor Curve For a certain distance D, the 100 Daily mileage distribution Utility Factor is the fraction of 90 total vehicle-miles-traveled Utility Factor curve (VMT) that occurs within the first 80 D miles of daily travel. For a 70

distance of 30 mi (48 km), the ) 60 utility factor is approximately (% y t i 40%. This means that an all- l 50 abi ob electric PHEV30 can displace r 40 petroleum consumption P equivalent to 40% of VMT, 30

(assuming the vehicle is fully 20 recharged each day). Similarly, an all-electric PHEV60 can 10 displace about 60%. This low- 0 0 20 40 60 80 100 daily-mileage characteristic is Daily Mileage (mi) why PHEVs have potential to displace a large fraction of per- Figure 1: Daily mileage distribution for US motorists based on vehicle petroleum consumption. the 1995 National Personal Transportation Survey

However, for PHEVs to displace fleet petroleum consumption, they must penetrate the market and extrapolate these savings to the fleet level. A second factor that is encouraging for PHEVs is the success of HEVs in the market. Global hybrid vehicle production is currently several hundred thousand units per annum [5]. Because of this, electric machines and high-power storage batteries are rapidly approaching maturity with major improvements in performance and cost having been achieved. Although HEV components are not optimized for PHEV applications, they do provide a platform from which HEV component suppliers can develop a range of PHEV components.

Finally, PHEVs are very marketable in that they combine the beneficial attributes of HEVs and battery electric vehicles (BEVs) while mitigating their disadvantages. Production HEVs achieve high fuel economy, but they are still designed for petroleum fuels and do not enable fuel substitution/flexibility. PHEVs, however, are true fuel-flexible vehicles that can run on petroleum or electrical energy. BEVs do not require any petroleum, but are constrained by battery technologies resulting in limited driving ranges, significant battery costs and lengthy recharging times. PHEVs have a smaller battery which mitigates battery cost and recharging time while the onboard petroleum fuel tank provides driving range equivalent to conventional and hybrid vehicles. This combination of attributes is building a strong demand for PHEVs, as evidenced by the recently launched Plug-In Partners Campaign [6].

57 PHEVs have the potential to come to market, penetrate the fleet, and achieve meaningful petroleum displacement relatively quickly. Few competing technologies offer this potential combined rate and timing of reduction in fleet petroleum consumption [7]. However, PHEV technology is not without its challenges. Energy storage system cost, volume, and life are major obstacles that must be overcome for these vehicles to succeed. Increasing the battery storage beyond that of HEVs increases vehicle cost and presents significant packaging challenges. Furthermore, the combined deep/shallow cycling in PHEV batteries is uniquely more demanding than that experienced by HEVs or BEVs. PHEV batteries may need to be oversized to last the life of the vehicle, further increasing cost. Given that HEVs are succeeding in the market, the question relevant to PHEVs is, “What incremental petroleum reductions can be achieved at what incremental costs?” These factors will critically affect the marketability of PHEVs through their purchase price and cost-of-ownership. This paper presents the results of a study designed to evaluate this cost-benefit tradeoff.

2 Modeling PHEV Petroleum Consumption and Cost

The reduction of per-vehicle petroleum consumption in a PHEV results from two factors: 1. Petroleum displacement during CD-mode, which as previously discussed relates to the PHEVx designation based on the added battery energy capacity of the vehicle. 2. Fuel-efficiency improvement in charge-sustaining (CS) mode due to hybridization, which relates to the degree-of-hybridization (DOH) or added battery power capability of the vehicle. HEVs, which do not have a CD-mode, are only able to realize savings via this second factor.

For a PHEVx, these two factors can be combined mathematically as follows: FC FC PHEVx = [1− UF ()x ] CS (1) FCCV FCCV where FCPHEVx is the UF-weighted fuel consumption of the PHEVx, FCCV is the fuel consumption of the reference conventional (non-hybrid) vehicle and FCCS is the PHEVx’s CS-mode fuel consumption. Note that this expression becomes approximate for PHEVs without all-electric capability because use of the utility factor in this way assumes that no petroleum is consumed in the first x miles of travel.

Figure 2 uses Equation 1 to compare the petroleum reduction of various PHEV designs. We see there are a variety of ways to achieve a target level of petroleum reduction. For example, a 50% reduction is achieved by an HEV with 50% reduced fuel consumption, a PHEV20 with 30% CS-mode reduction and by a PHEV40 with 0% CS-mode reduction (this last example is unlikely since PHEVs will show CS-mode improvement due to hybridization, notwithstanding the increase in vehicle mass from the larger battery). To demonstrate the feasible range of CS-mode reduction, Potential Reduction of Petroleum Consumption in PHEVs Figure 2 compares several 100% contemporary HEVs to their ) PHEV60 conventional counterparts (in the 90% PHEV40 on (%

i PHEV20 t

p 80% HEV case of the Toyota Prius, a m u s

n 70%

comparison is made to the Toyota o C 60% Corolla which has similar size and m eu 50% performance). At the low end of the rol t Challenging region

e Prius (Corolla) for HEV technology P 40% spectrum, the “mild” HEV Saturn n i

n Escape

o Civic Vue achieves a modest reduction of i 30%

ct Highlander Accord

less than 20%. The “full” HEV edu 20% R

l Vue a Toyota Prius achieves the highest t 10% o T percentage reduction (40%) of all 0% HEVs currently on the market 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% although, in addition to the platform Reduction in CS-mode Petroleum Consumption (%) enhancements employed in Figure 2: Potential per-vehicle reduction of petroleum production hybrids, it also uses an consumption in PHEVs

58 advanced (Atkinson-cycle) engine technology. Note that none of the production HEVs achieve the 50% reduction discussed in the above example, suggesting that there is an upper limit on the benefit of hybridization alone. Reductions exceeding 50% are available through CD-mode operation in a PHEV, although increasing PHEVx ranges can be seen to provide diminishing returns due to the nature of the Utility Factor curve (Figure 1).

The PHEV design space in Figure 2 characterized by CS/CD-mode fuel consumption has a matching space characterized by battery power/energy. Improving CS-mode fuel consumption implies an increase in DOH and battery power, while increasing CD-mode benefit implies an increase in PHEVx and useable battery energy. Moving in either direction incurs additional vehicle costs. However, the link between battery specifications, CS/CD-mode reductions, and vehicle costs is not obvious and must be explored through detailed vehicle fuel consumption and cost modeling. Therefore, a model was developed to predict the petroleum reductions and costs of contrasting PHEV designs compared to a reference conventional vehicle. The details of this model are presented in the following sections.

2.1 Modeling Approach and Scope of the Study The PHEV cost-benefit model includes several sub-models. First, a performance model calculates component sizes necessary to satisfy the performance constraints listed in Table 1. Second, a mass balance calculates the vehicle mass based on component sizes determined by the performance model. Third, an energy-use model simulates the vehicle’s gasoline and electricity consumption over various driving cycles. The vehicle performance and energy-use models are coupled to vehicle mass, so the model is able to capture mass compounding in the sizing of components. Fourth, a cost model estimates the vehicle retail price based on the component sizes. All costs are reported in 2006 US dollars. Finally, the results post-processing performs calculations to report the vehicle energy consumption and operating costs in meaningful ways. The model is implemented in an iterative Microsoft Excel spreadsheet.

The energy-use model is a detailed, second-by-second, dynamic vehicle model that uses a reverse- calculation approach [8]. It is also characterized as a power-flow model since it models component losses/efficiencies as functions of device power, rather than as functions of torque/speed or current/voltage as in more detailed models. This reverse-calculation, power-flow method provides rapid estimation of vehicle energy usage and enables the coupled, iterative spreadsheet described above. A solution is obtained in only a few seconds, meaning that the design space can be explored very quickly and thoroughly. Several hundred PHEV designs were therefore included in the study.

The model performs simulations of both conventional vehicles (CVs) and HEVs (including PHEVs) so that side-by-side comparisons can be made. The performance and energy-use models were validated for a Toyota Camry sedan and Honda Civic Hybrid. In both cases, errors of less than 5% were observed in the estimates of vehicle performance and energy use.

Two powertrain technology scenarios (Table 2) were included in the study. The near-term scenario (2005-2010) represents vehicles produced using current-status powertrain technologies, whereas the long-term scenario (2015-2020) allows for advanced technologies expected to result from ongoing R&D efforts and high-volume production levels. The long-term scenario does not, however, include advanced engine technologies since the author wanted to isolate the impact of improved electric drive and energy storage technologies on the relative cost-benefit of PHEVs.

2.2 Vehicle Platform, Performance and Cost Assumptions All vehicles included in the study satisfied the same performance constraints and used a vehicle platform identical to the baseline CV. The baseline CV was a midsize sedan (similar to a Toyota Camry or Chevrolet Malibu) and relevant parameters are presented in Table 1. Most parameters were calculated from sales-weighted average data for the top selling US midsize sedans in 2003 [9]. Some parameters, such as rolling resistance, accessory loads, passing acceleration, and gradeability, were engineering estimates. The baseline manufacturer’s suggested retail price (MSRP) of US$23,392 was

59 used in combination with the powertrain cost model to estimate the baseline “glider” cost (i.e. vehicle with no powertrain). The cost of a 121 kW CV powertrain was estimated at US$6,002, leading to an estimated baseline glider cost of US$17,390.

Table 1: Vehicle Platform and Performance Assumptions for Midsize Sedan Platform Parameters Glider Mass 905 kg Curb Mass 1429 kg Test Mass 1565 kg (136 kg load) Gross Vehicle Mass (GVM) 1899 (470 kg load) Drag coefficient 0.3 Frontal area 2.27m2 Rolling resistance coefficient 0.009 Baseline accessory load 800 W elec. (4000 W peak) Performance Parameters Standing acceleration 0-97 kph (0-60 mph) in 8.0 s Passing acceleration 64-97 kph (40-60 mph) in 5.3 s Top speed 177 kph (110 mph) Gradeability 6.5% at 88 kph (55 mph) at GVM with 2/3 fuel converter power Vehicle attributes Engine power 121 kW Fuel consumption 10.6 / 6.7 / 8.8 L per 100km (urban / highway / composite) MSRP $23,392

Table 2: Powertrain Technology Scenarios for the Cost-Benefit Analysis Near-Term Scenario Long-Term Scenario Battery Chemistry NiMH Li-Ion Module cost Twice that of long-term scenario $/kWh = 11.1 x P/E + 211.1 [14] Pack cost $ = ($/kWh + 13) x kWh + 680 [14] Same Li-Ion battery design function [15], Module mass NiMH battery design function [15], see Figure 6 see Figure 6 Tray/straps + thermal mgmt = 0.06 kg/kg [15] Pack mass Same Harness + bus bars = 0.14 kg/kW [15] Equivalent circuit model based on P/E ratio, Efficiency Same see Figure 5 SOC window SOC design window curve, see Figure 4 Same (assumes Li-Ion cycle life = NiMH) Motor Mass kg = 21.6 + 0.833 x kW [13] kg = 21.6 + 0.532 x kW [14] Cost $ = 21.7 x kW + 425 [14] $ = 16 x kW + 385 [14] Efficiency 95% peak efficiency curve, see Figure 5 Same Engine Mass kg =1.62 x kW + 41.8 [9] Same Cost $ = 14.5 x kW + 531 [14] Same Efficiency 34% peak efficiency curve, see Figure 5 Same

2.3 Powertrain Architecture BATTERY MOTOR The two things that differentiate a PHEV from an HEV are the inclusion of a CD operating mode and a recharging plug. ENGINE TRANS. Therefore, a PHEV can be implemented using any of the typical HEV architectures (parallel, series, or power- split). For this study, a parallel Figure 3: Parallel HEV powertrain architecture architecture was assumed with the ability to declutch the engine from the powertrain (Figure 3). This parallel layout provides greater flexibility in engine on/off control compared to Honda’s integrated motor assist (IMA) parallel system [10]

60 where the engine and motor are always connected. To create more flexibility in engine on/off control, it was also assumed that all accessories (including air conditioning) would be powered electrically from the battery.

2.4 Component Sizing Battery The battery is the first component sized by the model and the two key inputs are the PHEVx designation and the battery power-to-energy (P/E) ratio. The useable battery energy is calculated using an estimate of the vehicle’s equivalent electrical energy consumption per unit distance multiplied by the target PHEVx distance. The electrical energy consumption is estimated using the PAMVEC model [11]. The total battery energy is then calculated based on the SOC design window. Finally, the rated battery power is calculated by multiplying the total battery energy by the input P/E ratio and then de-rating by 20% to account for battery power degradation at end-of-life.

To achieve similar battery cycle life, different PHEVx ranges require different SOC design windows. The daily mileage distribution (Figure 1) means that a PHEV10 is far more likely to experience a deep cycle than a PHEV60. Therefore, the SOC design window must be 100% chosen such that the average daily 90%

SOC swing is consistent across the 80% Average daily SOC range of PHEVs. Figure 4 shows 70% swing based on daily the SOC design windows assumed Design SOC window mileage distribution 60% based on PHEVx in the PHEV cost-benefit model, 50% based on cycle-life data presented 40% by Rosenkrantz [12] and a target 30% battery life of 15 years (assuming 20% one full recharge each day). Figure 10% 4 also shows the resulting average Daily mileage probability distribution 0% daily SOC swing which is 0 102 03 04 05 0 60 consistent across the range. Daily Mileage / PHEVx

Electric Motor Figure 4: SOC design window for PHEVs The motor power is matched to the battery power, but with the resulting motor power being slightly smaller after accounting for electric accessory loads and motor/controller efficiency.

Engine Several steps are required to size the engine. First, the required peak power of the engine plus motor is calculated using the PAMVEC model [11]. This power is typically dictated by the standing acceleration performance and for the baseline midsize platform is approximately 120kW. The motor power is then subtracted from the total to provide a requirement for the engine power. This produces some “engine downsizing,” but there are downsizing limits imposed by other performance constraints. Continuous performance events (gradeability and top speed) determine the minimum permissible engine size. Gradeability performance is limited to 2/3 of peak engine power due to engine thermal management and noise, vibration, and harshness (NVH) considerations. For the baseline midsize platform, the minimum engine size is approximately 80kW.

2.5 Component Efficiencies, Masses, and Costs Engine and Electric Motor As discussed in section 2.1, the PHEV energy-use model is a reverse-calculation, power-flow model that simulates component losses/efficiencies as a function of output power. Both the engine and electric motor efficiencies are modeled using polynomial expressions for component input power as a function of output power. The engine curve is based on a 4-cylinder, 1.9L, 95kW gasoline engine. A 3rd-order polynomial was fitted to data from an ADVISOR simulation [8] using this engine. The

61 motor curve is based on a 50kW permanent magnet machine and a 9th-order polynomial was fitted to data from an ADVISOR simulation using this motor. Both efficiency curves are shown in Figure 5.

The engine and motor masses and costs are modeled as linear functions of rated output power. The engine mass function is derived from a database of 2003 model-year vehicles [9]. The near-term motor-controller mass function is based on the 2006 current Powertrain Components - Normalised Efficiency Curves status listed in the FreedomCAR 100% and Vehicle Technologies 90% Program Plan [13]. The long- term motor-controller mass is 80% based on technology 70% Engine demonstrated in the GM Precept 60% Motor-drive (%)

cy Motor-regen concept vehicle [14]. The n 50% e Battery ci engine cost function is based on 40% Effi manufacturers’ data provided to 30% the EPRI Hybrid-Electric 20% Vehicle Working Group (HEVWG) [14]. The near-term 10% and long-term motor cost 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% functions are also based on data Normalised Power (P/Pmax) reported by EPRI [14]. Figure 5: Efficiency curves used in the PHEV cost-benefit model Battery Battery efficiency is modeled using a normalized function for efficiency vs. input power (Figure 5). This relationship was derived from an equivalent circuit model using realistic values for nominal open- circuit voltage and internal impedance. Battery-module mass for both NiMH and Li-Ion technology is modeled using battery design functions developed by Delucchi [15] and shown in Figure 6. The added mass of battery packaging and thermal management was also based on [15].

Battery-module-specific costs ($/kWh) vary as a function of power-to-energy ratio (Figure 6). The long-term Li-Ion cost curve is based on estimates from EPRI [14]. After speaking with battery suppliers and other experts, it was estimated that the near-term specific cost of NiMH modules was approximately double that of EPRI’s long-term prediction. The costs of battery packaging and thermal management are also based on those listed in [14].

Recharging Plug and Charger PHEVs are assumed to be equipped with an inverter-integrated plug/charger with 90% efficiency and an incremental manufactured cost of US$380 over the baseline inverter cost [14].

Battery Design Functions Battery Cost Functions

5 20 2000 10 1200

1800 NiMH (near-term scenario) NiMH (near-term) LI-ION (long-term scenario) 1000 1600 ) Li-Ion (long-term) Wh ) g 1400 /k k $ / 800 ( t W 1200 s o r ( C we c

5 i o 1000 f 600 i ec ic P

if 800 c e

e Sp 400 p 600 S dul

400 2 Mo 200 200

0 0 0 50 100 150 200 0 5 10 15 20 25 30 Specific Energy (Wh/kg) Power-to-Energy Ratio (1/h) Figure 6: Battery design functions and module cost curves assumed for NiMH and Li-Ion technology

62 Retail Markup Factors The component cost functions in Table 2 model the manufactured cost of components. To convert these to retail costs in a vehicle, various markup factors are applied. A manufacturer’s markup of 50% and dealer’s markup of 16.3% are assumed based on estimates by EPRI [14]

2.6 Powertrain Control Strategy A generic control strategy was developed for the spectrum of PHEV designs. This control strategy consists of four basic elements. The basis of the strategy is an SOC-adjusted engine power request:

Pengine−request = Pdriveline − k(SOC − SOCt arg et ) (2)

When the SOC is higher than the target, the engine power request is reduced to promote CD operation. Alternatively, when the SOC is lower than the target, the engine power request is increased to recharge the battery. The adjustment is governed by the factor k which is set proportional to total battery capacity. An electric-launch speed of 10 mph (16 kph) is also specified, below which the strategy tries to operate the vehicle all-electrically by setting the engine power request to zero. However, both the SOC adjustment and electric launch can cause the power ratings of the motor to be exceeded. Therefore, a third element of the strategy is to constrain the engine power request to within acceptable limits such that no components are overloaded. Finally, there is engine on/off control logic. The engine is triggered on whenever the adjusted engine power request becomes positive. Once on, however, the engine can only turn off after it has been on for at least 5 minutes. This final constraint is designed to ensure the engine warms up thoroughly so that repeated cold starts are avoided.

The aim of this control strategy is to prioritize discharging of the battery pack. Given the nature of the daily mileage distribution, this approach ensures that the maximum petroleum will be displaced. However, the strategy does not explicitly command all-electric operation. Rather, it discharges battery energy at the limits of the battery/motor power capabilities and uses the engine as needed to supplement the road load power demand. Therefore, the vehicle behavior that results is totally dependent on the power ratings of components. Vehicles with higher electric power ratings will have all-electric capability in more aggressive driving, whereas vehicles with lower electric power ratings will tend to operate in a “blended” CD-mode that utilizes both motor and engine. For more discussion of all-electric vs “blended” operation, the reader is directed to [16].

2.7 Driving Cycles The cost-benefit model simulates CVs, HEVs, and PHEVs over two cycles – the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET) – used by the US Environmental Protection Agency (EPA) for fuel economy and emissions testing and labeling [17].

2.8 Fuel Economy Measurement and Reporting The PHEV fuel economies and operating costs are measured and reported using a procedure based on a modification of the Society of Automotive Engineers' (SAE) J1711 Recommended Practice for Measuring the Exhaust Emissions and Fuel Economy of Hybrid-Electric Vehicles [18]. This procedure measures the fuel and electricity use in both CD and CS-modes and weights them according to the Utility Factor (UF), assuming the PHEVs are fully-recharged each day. Further discussion of this procedure for fuel economy measurement and reporting is provided in [17].

3 Results PHEV2, 5, 10, 20, 30, 40, 50, and 60 vehicles were considered in the study. Also, an HEV0 was modeled as a PHEV2 with its charger/plug removed. P/E ratios were chosen to vary DOH (defined as the ratio of motor power to total motor plus engine power) across a range of approximately 10%–55%. Note that the engine downsizing limit corresponds to a DOH of approximately 32%, and that DOH higher than this results in excess electric power capability onboard the vehicle.

63 Battery Power vs Energy for PHEVs

Figure 7 shows the battery 20 10 6 4 120 specifications for the spectrum of PHEV2 PHEVs in the long-term scenario. 100 The total battery energy varies PHEV5

from approximately 1.5 kWh for ) 80 PHEV10 the HEV0/PHEV2 to UDDS all-electric PHEV20 2 approximately 25kWh for the 60 PHEV30

PHEV60. The battery power ery Power (kW t t a

B 40 varies from approximately 10– UDDS PHEV40 100kW across the range of DOH. blended 1 PHEV50 Figure 7 includes dashed lines of 20 constant P/E ratio, which varied PHEV60 from approximately 1–50. 0 0 5 10 15 20 25 30 Figure 7 also indicates the Total Battery Energy (kWh) minimum battery power Figure 7: Battery specifications for the spectrum of PHEV requirement (approximately designs (long-term scenario) 45kW) for the PHEVs to have all-electric capability on the UDDS test cycle. The battery specifications for the near-term scenario are similar to Figure 7 but have increased power and energy requirements due to mass-compounding from the lower specific energy of NiMH batteries.

Figure 8 presents the reductions Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans in annual petroleum consumption $20,000 HEV0 and incremental costs for the $18,000 spectrum of PHEVs in the long- PHEV2 $16,000 term scenario. Taking a PHEV5 $14,000 PHEV10 macroscopic view, we see that ent

em $12,000 r PHEV20 increasing PHEVx provides c n I

t $10,000 increasing reduction in PHEV30

il Cos $8,000 petroleum consumption. a PHEV40

Relative to the baseline CV, Ret $6,000 PHEV50 which consumes 659 gal (2494 $4,000 L) of petroleum based on 15,000 PHEV60 $2,000 UDDS AER mi (24,100 km) each year, the vehicles $- HEVs reduce petroleum 0 100 200 300 400 500 600 consumption by 20%–28%. The Reduction in Annual Petroleum Consumption (gals.) PHEVs reduce petroleum Figure 8: Incremental costs and annual petroleum consumption consumption further, ranging for the spectrum of PHEV designs (long-term scenario) from 21%–31% for the PHEV2s up to 53%–64% for the PHEV60s. However, these increasing reductions come at increasing costs. The HEV0s are projected to cost US$2,000–$6,000 more than the baseline CV, whereas the PHEV60s are projected to cost US$12,000–$18,000 more. The near-term trend is quite similar to Figure 8, except that petroleum reductions are slightly reduced and vehicle cost increments are much larger due to the greater mass and significantly higher cost of near-term NiMH batteries.

Looking closely at Figure 8, we see a repeated trend in the relative cost-benefit of PHEVs with varying DOH, and there is an optimum DOH for each PHEVx. For the HEV0s, the optimum DOH (32%) coincides with the limit of engine downsizing. For the PHEVs, the optimum DOH is higher (35%) to coincide with the minimum battery power required for all-electric capability on the UDDS cycle (the maximum power requirement on the HWFET cycle is lower). This all-electric capability allows vehicles to avoid engine idling losses that would otherwise be incurred due to engine turn-on events subject to the 5-minute minimum engine on time constraint. The optimum HEVs and PHEVs for the near-term and long-term scenarios are summarized in Tables 3 and 4.

64 It must be emphasized that these optimum DOH are highly-dependent on the vehicle platform/performance attributes and the nature of the driving pattern. The analysis should be repeated for other baseline vehicles (e.g. sport-utility vehicles) to see how the PHEV designs will vary. Furthermore, PHEVs should be simulated over real-world driving cycles to identify differences in the petroleum displacement and all-electric operation compared to standard test cycles. Such further analyses should provide the understanding needed to optimize PHEVs for the market.

Table 3: Near-Term Scenario PHEV Specifications – Optimum DOH Vehicles Vehicle Curb Engine Motor DOH Battery P/E SOC Fuel Elec. Retail Mass Power Power Energy Ratio Window Cons. Cons. Cost (kg) (kW) (kW) (kWh) (1/h) (L/100km) (Wh/km) (US$) CV 1429 122 ------10.3 --- 23,392 HEV0 1451 78 38 33% 1.5 33.4 37% 7.5 --- 28,773 PHEV2 1451 78 38 33% 1.5 33.4 37% 7.3 7 29,435 PHEV5 1505 80 42 35% 3.6 15.9 39% 7.1 17 31,447 PHEV10 1571 82 44 35% 6.9 8.6 41% 6.7 33 34,180 PHEV20 1678 85 47 35% 12.7 4.9 47% 6.0 60 38,935 PHEV30 1759 89 49 36% 17.2 3.8 53% 5.4 84 42,618 PHEV40 1824 91 51 36% 20.8 3.3 59% 4.8 104 45,655 PHEV50 1880 94 52 36% 23.9 2.9 66% 4.5 118 48,162 PHEV60 1923 96 53 36% 26.4 2.7 73% 4.1 133 50,184

Table 4: Long-Term Scenario PHEV Specifications – Optimum DOH Vehicles Vehicle Curb Engine Motor DOH Battery P/E SOC Fuel Elec. Retail Mass Power Power Energy Ratio Window Cons. Cons. Cost (kg) (kW) (kW) (kWh) (1/h) (L/100km) (Wh/km) (US$) CV 1429 122 ------10.3 --- 23,392 HEV0 1412 77 36 32% 1.5 32.8 37% 7.4 --- 26,658 PHEV2 1412 77 36 32% 1.5 32.8 37% 7.2 7 27,322 PHEV5 1445 78 41 34% 3.5 15.7 39% 7.0 17 28,365 PHEV10 1481 79 42 35% 6.6 8.5 41% 6.5 32 29,697 PHEV20 1531 81 43 35% 11.8 4.9 47% 5.7 58 31,828 PHEV30 1569 82 44 35% 15.9 3.7 53% 5.0 78 33,533 PHEV40 1598 83 45 35% 19.0 3.2 59% 4.5 96 34,839 PHEV50 1618 84 45 35% 21.6 2.8 66% 4.1 108 35,857 PHEV60 1636 84 46 35% 23.6 2.6 73% 3.7 120 36,681

3.1 Economics of PHEVs The PHEV cost-benefit analysis also includes a simple comparison of cost-of-ownership over the vehicle lifetime. The comparison includes the retail cost of the vehicle and the cost of its annual energy (fuel and electricity) consumption, but does not account for possible differences in maintenance costs (for a more thorough analysis of total PHEV lifecycle costs, the reader is directed to [14]). Figure 9 presents economic comparisons for the near-term and long-term scenarios. In calculating annual petroleum and electricity consumption, all vehicles are assumed to travel 15,000 mi (24,100 km) per year to be consistent with the assumptions of the US EPA. The near-term cost of retail gasoline is assumed to be US$3 per gallon (US$0.79 per L), whereas a higher gasoline cost of US$5 per gallon (US$1.32 per L) is assumed for the projected scenario. The cost of retail electricity is held constant at US$0.09 per kWh based on the 2005 US average retail price and historical trends [19]. No discount rate was applied to future cash flows.

In the near-term scenario, the HEV achieves a lower cost-of-ownership than the CV after approximately 10 years. However, the PHEVs never achieve a lower cost-of-ownership than the CV nor the HEV over the 15-year vehicle lifetime. The long-term scenario provides a significant contrast, with the HEV providing lower cost than the CV after approximately 4 years and the PHEVs providing lower cost than the HEV after approximately 12 years.

65 Cumulative Vehicle plus Energy (Fuel/Elec.) Costs Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$60,000 $60,000

$50,000 $50,000

$40,000 $40,000 st st o o C C ve ve

i PHEV40 i PHEV40 t $30,000 t $30,000

la PHEV20 la PHEV20 u u

m PHEV10 m PHEV10 u u

C $20,000 HEV0 C $20,000 HEV0 Near-term CV Long-term CV $10,000 $10,000

$- $- 05 1 015 0 5 1 015 Years after purchase Years after purchase Figure 9: Economic comparison of PHEVs in the near-term and long-term scenarios Several observations can be made from these comparisons. It is clear that these “payback” analyses are sensitive to the cost of gasoline and also the vehicle retail costs, which are strongly affected by the battery cost assumptions in each scenario. It is also clear that the economics of PHEVs are not promising if gasoline prices remain at current levels and battery costs cannot be improved. However, it does seem that a compelling business case for plug-in hybrids can be made under a scenario of both higher gasoline prices and projected (lower) battery costs, at least from the perspective of the simple consumer economic comparison presented here.

Despite the uncertainty of PHEV economics, there are other factors that may justify the incremental PHEV cost. Examples include tax incentives; reductions in petroleum use, air pollution, and greenhouse emissions; national energy security; reduced maintenance; fewer fill-ups at the gas station; convenience of home recharging; improved acceleration from high-torque electric motors; a green image; opportunities to provide emergency backup power in the home; and the potential for vehicle-to- grid applications. Alternative business models—such as battery leasing—also deserve further consideration since they might help to mitigate the daunting incremental vehicle cost and encourage PHEV buyers to focus on the potential for long-term cost savings.

4 Conclusion This paper has presented a comparison of the costs (vehicle purchase costs and energy costs) and benefits (reduced petroleum consumption) of PHEVs relative to HEVs and CVs. Based on the study results, there is a very broad spectrum of HEV-PHEV designs with greatly varying costs and benefits. Furthermore, the PHEV cost-benefit equation is quite sensitive to a range of factors. In particular, battery costs, fuel costs, vehicle performance, and driving habits have a strong influence on the relative value of PHEVs. Given the large variability and uncertainty in these factors, it is difficult to predict the future potential for PHEVs to penetrate the market and reduce fleet petroleum consumption.

However, the potential for PHEVs to reduce per-vehicle petroleum consumption is clearly very high. Reductions in excess of 45% are available using designs of PHEV20 or higher. This compares favorably with the 30% maximum reduction estimated for HEVs However, it seems likely that the added battery capacity of a PHEV will result in significant vehicle cost increments, even in the long term. For the projected scenario in this study, a retail cost increment of US$3,000 was estimated for a midsize sedan HEV. In contrast, the long-term cost increments for a midsize PHEV20 and PHEV40 were estimated at US$8,000 and US$11,000 respectively. Without knowing the future costs of petroleum, it is impossible to determine the future economics of PHEVs. But it does seem likely, based on the results of this study, that it will be quite a challenge to justify the PHEV capital cost premium on the basis of reduced lifetime energy costs alone. Other incentives and business models may be required to create an attractive value proposition for PHEV motorists. However, the large petroleum reduction potential of PHEVs offers significant national benefits and provides strong justification for governmental support to accelerate the deployment of PHEV technology.

66 Acknowledgement The authors would like to acknowledge the programmatic support of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program.

References [1] Sanna, L., "Driving the Solution: The Plug-In Hybrid Vehicle." EPRI Journal, 2005. [2] California Air Resources Board, "California Exhaust Emission Standards and Test Procedures for 2005 and Subsequent Model Zero-Emission Vehicles, and 2002 and Subsequent Model Hybrid Electric Vehicles, in the Passenger Car, Light-Duty Truck and Medium-Duty Vehicle Classes." California EPA, 2003. [3] "Plug-In Hybrids." California Cars Initiative online, www.calcars.org/vehicles.html. Accessed July 30, 2006. [4] 1995 National Personal Transportation Survey (NPTS), npts.ornl.gov/npts/1995/doc/index.shtml. [5] "Sales Numbers" hybridCARS.com online, www.hybridcars.com/sales-numbers.html. Accessed July 30, 2006. [6] Plug-In Partners online, http://www.pluginpartners.org/. Accessed July 30, 2006. [7] Markel, T.; O'Keefe, M.; Gonder, J.; Brooker, A.. Plug-in HEVs: A Near-term Option to Reduce Petroleum Consumption. NREL 39415. Golden, CO: National Renewable Energy Laboratory, 2006. [8] Wipke, K.B.; Cuddy, M.R.; Burch, S.D. "ADVISOR 2.1: A User-Friendly Advanced Powertrain Simulation Using a Combined Backward/Forward Approach." IEEE Transactions on Vehicular Technology; Vol 48, No. 6, 1999; pp. 1751-1761. [9] Rush, D. Market Characterization for Light Duty Vehicle Technical Targets Analysis, National Renewable Energy Laboratory, 2003. [10] "Honda IMA System/Power Unit." Honda online, world.honda.com/CIVICHYBRID/Technology/NewHondaIMASystem/ PowerUnit/index_1.html, accessed July 30, 2006. [11] "PAMVEC Model." University of Queensland Sustainable Energy Research Group online, www.itee.uq.edu.au/~serl/PAMVEC.html, accessed July 30, 2006. [12] Rosenkrantz, K. "Deep-Cycle Batteries for Plug-In Hybrid Application." EVS20 Plug-In Hybrid Vehicle Workshop, Long Beach, 2003. [13] "Multi-Year Program Plan." FreedomCAR and Vehicle Technologies Program online. http://www.eere.energy.gov/vehiclesandfuels/resources/fcvt_mypp.html, accessed July 30, 2006. [14] Graham, R. et al. "Comparing the Benefits and Impacts of Hybrid Electric Vehicle Options." Electric Power Research Institute (EPRI), 2001. [15] Delucchi, M. "Electric and Gasoline Vehicle Lifecycle Cost and Energy-Use Model." Institute of Transportation Studies. University of California, Davis, 2000. [16] Markel, T.; Simpson, A.; "Plug-In Hybrid Electric Vehicle Energy Storage System Design," Proc. Advanced Automotive Battery Conference; 2006, Baltimore, Maryland. [17] Gonder, J.; Simpson, A. "Measuring and Reporting Fuel Economy of Plug-In Hybrid Electric Vehicles," 22nd International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium & Exposition; 2006, Yokohama. [18] "SAE J1711 – Recommended Practice for Measuring Fuel Economy of Hybrid-Electric Vehicles." Society of Automotive Engineers Surface Vehicle Recommended Practice. Society of Automotive Engineers, Warrendale, 1999. [19] U.S. Energy Information Administration online, www.eia.doe.gov, accessed July 30, 2006.

Author Andrew Simpson, Vehicle Systems Engineer, National Renewable Energy Laboratory (NREL), 1617 Cole Blvd, Golden CO 80401 USA; Tel: 303-275-4430; Fax: 303-275-4415; [email protected]. Andrew joined the Advanced Vehicle Systems Group at NREL in 2005 and his current focus is plug-in hybrid-electric vehicles. He holds a Bachelor of Mechanical Engineering (2000) and Ph.D. in Electrical Engineering (2005) from the University of Queensland, Brisbane, Australia. Prior to NREL, Andrew worked as a CFD consultant for Maunsell Australia. He also co-founded the Sustainable Energy Research Group at The University of Queensland and was a coordinating member of the University’s “SunShark” solar car team which raced successfully from 1996-2000.

67 Section 2.3

Title: “Plug-In Hybrid Electric Vehicles: Current Status, Long-Term Prospects, and Key Challenges” Type: Presentation Authors: Matthew Thornton, Tony Markel, Andrew Simpson, Jeff Gonder, Aaron Brooker Date: July 20, 2006 Conference or Meeting: Presented at the Federal Network for Sustainability Meeting at Idaho National Laboratory Abstract: Provides an overview of PHEV technology, technical challenges, and systems analysis efforts

68 Plug-in Hybrid Electric Vehicles Current Status, Long-Term Prospects and Key Challenges

Matthew Thornton Tony Markel, Andrew Simpson, Jeff Gonder, Aaron Brooker

Federal Network for Sustainability Meeting July 20, 2006 Idaho National Laboratory

U.S. Department of Energy FreedomCAR and Vehicle Technologies Program Vehicle Systems Subprogram Vehicle Systems Analysis Activity Lee Slezak, Technology Manager

69 The Perfect Storm

Domestic Production Domestic Consumption 20

• Petroleum consumption has steadily ) y a d / increased while domestic production has b 15 mm

continued to decline m ( u

e 10 l o r • World oil production predicted to peak t Pe within the next 5-15 years 5 Source: U.S. Department of Energy, Energy Information Administration 0 • Recent increase in gasoline price is 1940 1950 1960 1970 1980 1990 2000 2010 indicator of growing tension between supply and demand Gasoline price - 85% rise in 5 years! 3.5

3 e n li

aso 2.5 e G al) /g ag $ r 2 e e ( v ic A r l

a Source: Hubbert Center Newsletter #99/1 R. Udall and S. Andrews n

il P 1.5 o a i t a Ret N 1 ekly

e WHAT’S OUR PLAN? W 0.5

Source: U.S. Department of Energy, Energy Information Administration 0 3 Aug 11, May 07, Jan 31, Oct 28, Jul 24, Apr 19, Jan 14, Oct 10, Jul 06, 1987 1990 1993 1995 1998 2001 2004 2006 2009

America’s Oil Addiction

• Transportation sector nearly 100% petroleum dependent • Transportation accounts for two-thirds of total petroleum consumption • 58% of petroleum is imported “America is addicted to oil…”

The President’s Advanced Energy Initiative Goals: Fueling Our Vehicles 1. Develop advanced battery technologies that allow a plug-in hybrid-electric vehicle to have a 40-mile range operating solely on battery charge 2. Foster the breakthrough technologies needed to make cellulosic ethanol cost- competitive with corn-based ethanol by 2012. 3. Accelerate progress towards the President’s goal of enabling large numbers of Americans to choose hydrogen fuel cell vehicles by 2020.

4 Source: www.whitehouse.gov

70 A “Full” Hybrid

ADVANCED ENGINE

ELECTRIC ACCESSORIES

ENGINE DOWNSIZING

PETROLEUM ONLY

REGENERATIVE BRAKING ENGINE IDLE-OFF

76hp gasoline engine, 67hp electric motor, 1.5kWh battery 5

Contemporary Hybrids

Toyota Camry Toyota Prius Toyota Highlander Honda Insight

Honda Civic Honda Accord Lexus RX400h Lexus GS450h

Chevy Silverado Saturn Vue Ford Escape

71 A Plug-In Hybrid

BATTERY RECHARGE Fuel Flexibility

ADVANCED ENGINE

ENGINE IDLE-OFF

PETROLEUM ENGINE DOWNSIZING AND/OR

ELECTRICITY ELECTRIC ACCESSORIES

REGENERATIVE BRAKING

76hp gasoline engine, 67hp electric motor, 9.0kWh battery (30mi) 7

OEM Plug-In Hybrids

2003 Renault Kangoo Elect’road - up to 50mi electric range - approximately 500 sold in Europe

Renault Cleanova - Max range 329 miles with 5.3 gallons of fuel

DaimlerChrysler Sprinter PHEV - 15 prototypes being produced for testing in various locations in Europe and North America - up to 20mi electric range 8

72 Other PHEV Prototypes - Industry

EnergyCS Plug-In Prius

HyMotion Escape PHEV

Oil Use Reduction with HEVs

Light Duty Fleet Oil Use - Impact of HEVs on Consumption

16 2005 AEO Base Case HEV Scenario 14

12 Oil use same as today! BD)

P 10 3 MBPD M on ( i

pt 8 um

6 Cons l i O 4 This highly aggressive scenario assumes 100% HEV sales from 2010 onwards… 2

0 2000 2005 2010 2015 2020 2025 2030 Year HEVs unable to reduce consumption below today’s consumption level 10 Produced using VISION model, MBPD = million barrels per day

73 Oil Use Reduction with PHEVs

Light Duty Fleet Oil Use - Impact of PHEVs on Consumption

16 2005 AEO Base Case PHEV Scenario 14

12 Oil use reduction! BD)

P 10 4 MBPD M on ( i

pt 8 um

6 Cons l i

O PHEVs on E85 ?? 4 This highly aggressive scenario assumes 100% HEV sales from 2010 and 50% PHEV40 sales from 2020 onwards… 2

0 2000 2005 2010 2015 2020 2025 2030 Year PHEVs reduce oil consumption with a transition to electricity 11 Produced using VISION model, MBPD = million barrels per day

Design Options All-Electric vs Blended Strategy All-Electric

70 100% engine • Engine turns on when battery 60 motor 90% SOC reaches low state of charge 50 80% • Requires high power battery and 40 70%

) 30 60% motor ) W k % ( 20 50% ( wer SOC

Po 10 40%

0 30% -10 20% Blended

-20 10% 70 100% engine -30 0% 60 motor 90% 0 5 10 15 20 25 30 35 40 SOC 50 80% Distance (mi) 40 70%

) 30 60% ) W k % ( r

20 50% C ( e

• Engine turns on when power SO Pow 10 40%

exceeds battery power capability 0 30% • Engine only provides load that -10 20% exceeds battery power capability -20 10% -30 0% 0 5 10 15 20 25 30 35 40 Distance (mi) 12

74 Plug-In Hybrid Fuel Economy

Predicted fuel economy and operating costs for midsize sedan1

Vehicle Type Gasoline Electricity Annual Annual Recharge Fuel Use Energy Use Energy Cost Time3 Economy Conventional 27 mpg --- 564 gal. $1360 ---

Hybrid-Electric 36 mpg --- 416 gal. $1000 ---

Plug-In Hybrid 51 mpg 0.09 kWh/mi 297 gal. and $716 + $125 < 4 hrs 20mi range 1394 kWh2 Plug-In Hybrid 69 mpg 0.16 kWh/mi 218 gal. and $525 + $211 < 8 hrs 40mi range 2342 kWh2 1) Assumes 15,000 miles annually, gasoline price of $2.41 per gallon, electricity price of 9c/kWh 2) Note that average US household consumes 10,700 kWh of electricity each year 3) Using 110V, 20A household outlet

Household Travel Survey Data Can be Used to Predict Real-World Benefits of Advanced Technologies • Provides valuable insight into travel behavior • GPS augmented surveys supply details needed for vehicle simulation

75 PHEVs Reduce Fuel Consumption By >50% On Real- World Driving Cycles

227 vehicles from St. Louis each modeled as a conventional, hybrid and PHEV

40 400 Conventional Average Daily Costs Hybrid 26 mpg ) 35 350 ) Gas. Elec. ¢/mi

% PHEV20

( PHEV40 e CV $3.45 --- 9.1 lons l s

30 300 a U g HEV $2.48 --- 6.5 n ( I d t 37 mpg e 25 • 8647 total miles driven 250 PHEV20 $1.58 $0.48 5.4 e l

• 100% replacement of ume F

s PHEV40 $1.21 $0.72 5.1 e sample fleet cl 20 200 i 58 mpg & h Con

HEVs and PHEVs Likely to Reduce Greenhouse Emissions

16 Source: Hybrid Electric Vehicle Working Group, www.epri.com

76 Electrified Miles May Lead to Cleaner Operation

17 Source: Hybrid Electric Vehicle Working Group, www.epri.com

Technical Challenges Battery Life

• PHEV battery likely to deep-cycle each day driven: 15 yrs equates to 4000-5000 deep cycles • Also need to consider combination of high and low frequency cycling

70%

50%

4000

Data presented by Christian Rosenkranz (Johnson Controls) at EVS 20 18

77 Technical Challenges Battery Packaging

Technical Challenges Vehicle Costs

100%

90% HOW CAN WE SAVE THE MOST GALLONS AT THE LEAST COST? 80%

70% (%)

st 60% o

50% Prius (Corolla)

ental C PHEVs? m

e 40% Civic cr HEVs n Escape I 30% Highlander 20% Accord 10% Vue 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Gasoline Savings (%) 20

78 Vehicle Costs

Projected Retail Powertrain Costs - Midsize Sedans 1) including manufacturer and dealer markups 2) all component costs assume projected status

$18,000 charger/plug $663 $16,000 battery motor/inverter $14,000 transmission $663 engine

$12,000 $8,433

$10,000 $5,117

$8,000 $2,433

$2,454 $6,000 $2,079 $2,407 $1,998 $4,000 $1,992 $2,004 $2,020

$2,000 $4,005 $2,865 $2,919 $3,002

$- CV HEV PHEV20 PHEV40

Vehicle Costs cont.

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$60,000

CV HEV $50,000 PHEV20 PHEV40

$) $40,000 6 0 0 2 ( t $30,000 cos ve i t a l u $2.$3.$4.00/41/gal. (t(2005oday) avg) m

u $20,000 C

$10,000

$- 0 5 10 15 Time after purchase (years)

79 Vehicle Costs cont.

Why would PHEV buyers pay more? • Tax incentives

• Reduced petroleum use, air pollution and CO2 • National energy security • Less maintenance • Reduced fill-ups • Convenience of home recharging (off-peak) • Improved acceleration (high torque of electric motors) • Green image, “feel-good factor”

Our Contributions

• Developing Route-Based control strategies that will help extend the life of vehicle batteries • Evaluating alternative battery usage and ownership scenarios to reduce costs — Does it really have to last the life of the vehicle — Battery lease or combine with home mortgage • Using travel data to develop real-world usage pattern to aid in effective system design • Vehicle thermal management for PHEVs — Energy storage – protection for life — Cabin – passenger comfort — Engine – emissions control

80 Conclusions

• Plug-in hybrid technology uses electricity from the utility grid to reduce petroleum consumption beyond that of HEV technology — Predicted 50% reduction in in-use consumption based on simulations using travel survey data • Industry interest is growing and some prototypes have been built — Collaboration between labs and industry will likely lead to innovative systems solutions • The U.S. Department of Energy is expanding its research portfolio to include PHEVs — Research will address key remaining barriers to commercial PHEVs including battery life, packaging, and cost

81 Section 2.4

Title: “Plug-In Hybrid Electric Vehicle Energy Storage Analysis” Type: Presentation Authors: Andrew Simpson and Tony Markel Date: July 25, 2006 Conference or Meeting: Presented to the FreedomCAR Electrochemical Energy Storage Technical Team PHEV Working Group Meeting Abstract: Provides a summary of cost and consumption benefit analysis as it relates to setting requirements for energy storage systems for PHEVs

82 Plug-In Hybrid Electric Vehicle Energy Storage Analysis spacer (with implications for vehicle fuel economy and cost) By Andrew Simpson and Tony Markel Vehicle Systems Analysis Team National Renewable Energy Laboratory

Plug-In Hybrid Battery Workgroup Meeting Electrochemical Energy Storage Tech Team USCAR, Southfield, MI

July 25, 2006

With support from Lee Slezak, Vehicle Systems Program FreedomCAR and Vehicle Technologies Program Office of Energy Efficiency and Renewable Energy U.S. Department of Energy

83 NREL’s Plug-in HEV R&D Activities

• Battery Level — R&D support to developers — Testing and evaluation — EPRI/Sprinter PHEV testing — Thermal characterization and design • Vehicle Level — Real-world PHEV simulations - fuel economy and recharging — Development of test procedures for PHEVs and MPG reporting — Evaluation of alternative PHEV design strategies — PHEV design cost-benefit analysis • Utility Level — Assessment of PHEV impacts on utilities — Exploring synergies between PHEVs and wind power — V2G opportunities for PHEVs in regulation services • National Level — Benefits assessment - oil use and emissions — Renewable community – linking PHEV to renewable • Analysis support to DOE, OEMs, and others — Working to identify and overcome barriers to PHEV Secretary of Energy visiting NREL on adoption 7/7/06 for ribbon cutting of the new S&T Facility and then discussing plug-in hybrids with EnergyCS & Hymotion 3

Key Messages

In setting energy storage requirements for PHEVs, we need to consider: 1. There is a VERY large spectrum of PHEV designs 2. There is a link between SOC window, driving habits and cycle life that impacts energy storage size and cost 3. A wide range of P/E ratios may be needed 4. Tradeoffs between fuel economy / oil displacement and powertrain mass, volume and cost

Fundamental Question: What kind of PHEV are we setting requirements for?

84 Some PHEV Definitions

Charge-Depleting (CD) Mode: An operating mode in which the energy storage SOC may fluctuate but on-average decreases while driving

Charge-Sustaining (CS) Mode: An operating mode in which the energy storage SOC may fluctuate but on-average is maintained at a certain level while driving

All-Electric Range (AER): After a full recharge, the total miles driven electrically (engine-off) before the engine turns on for the first time.

Zero Emission Vehicle (ZEV) Range: The same as all-electric range (AER), no tail-pipe emissions during EV mode. To qualify for CARB ZEV, the minimum ZEV or AER should be 10 miles during the UDDS drive cycle.

EV, HEV and Blended Modes and Range:

Electric Vehicle Miles (EVM): After a full recharge, the total miles driven electrically (engine-off) before the vehicle reaches charge-sustaining mode

Charge-Depleting Range (CDR): After a full recharge, the total miles driven before the vehicle reaches charge-sustaining mode.

PHEV20: A PHEV with useable energy storage equivalent to 20 miles of driving energy on a reference driving cycle. The PHEV20 can displace petroleum energy equivalent to 20 miles of driving on the reference cycle with off-board electricity.

NOTE: PHEV20 does not imply that the vehicle will achieve 20 miles of AER, EVM or CDR on the reference cycle nor any other driving cycle. Operating characteristics also depend on the power ratings of components, the powertrain control strategy and the nature of the driving cycle

PHEV Architectures

A PHEV can be implemented using any of the HEV architectures (parallel, series or power-split).

The two things that differentiate PHEVs from HEVs are a CD operating mode and a recharging plug.

BATTERY MOTOR

ENGINE TRANS.

The results shown in this presentation are for a parallel PHEV.

85 PHEV Design Strategies: All-Electric vs Blended

All-Electric

70 100% engine Í Requires higher-power 60 motor 90% SOC battery and motor 50 80%

40 70% Í Engine turns on when battery

) 30 60% reaches CS state of charge ) W k % ( 20 50% ( wer SOC

Po 10 40%

0 30% -10 20% Blended

-20 10% 70 100% engine -30 0% 60 motor 90% 0 5 10 15 20 25 30 35 40 SOC 50 80% Distance (mi) 40 70%

) 30 60% ) W k % ( r

20 50% C ( Î Uses lower-power battery and e SO motor Pow 10 40% 0 30%

Î Engine turns on when power -10 20% request exceeds battery power limit -20 10% -30 0% 0 5 10 15 20 25 30 35 40 Distance (mi) 7

PHEV Design Strategies (cont.)

Benefits of using a blended strategy: Lower power to energy ratio leads to lighter, smaller, and cheaper energy storage system.

Battery Design Functions Battery Module Cost Functions

2000 1200 NiMH 1800 Typical LI-ION Near Term 1000 Estimation 1600 Long Term ) ) g

1400 h /k 800 W W /k ( 1200 r e t ($ s w o 1000 o 600 P C le

ific 800 u c d e

o 400 p 600 M S 400 200 200

0 0 0 50 100 150 200 0 5 10 15 20 25 30 Specific Energy (Wh/kg) Power-to-Energy (P/E) Ratio

86 PHEV Design Spectrum

what’s our target?

volume cycle life mass self discharge battery power calendar life (degree of gasoline displaced hybridization) cost Voltage range fuel economy temperature capacity

vehicle attributes battery energy (PHEVx)

Exploring the PHEV Design Spectrum

Ongoing NREL study using validated CV/HEV/PHEV simulation model

Key Inputs: Cost / Benefit Analysis of Hybrid-Electric and Vehicle platforms and Plug-In Hybrid-Electric Vehicle Technology performances SOC design window

A presentation to the FreedomCAR Vehicle Systems Analysis Technical Team by Control strategy Battery performance and cost Andrew Simpson models National Renewable Energy Laboratory Other component performance Presented Wednesday, 1st March 2006 REVISED May 2006 and cost models Plug-in Hybrid Electric Vehicle Energy With support from the U.S.SDetpartormentage of Energ Sy ystem Design Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program Advanced Automotive Battery Conference Baltimore, Maryland May 19th, 2006

by Tony Markel and Andrew Simpson National Renewable Energy Laboratory

With support from the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program

87 Baseline Vehicle Characteristics – Midsize Sedan

MIDSIZE SEDAN (AUTOMATIC)

Platform Parameters

Glider Mass 905 kg

Curb Mass 1429 kg

Test Mass 1565 kg (136 kg load)

Gross Vehicle Mass (GVM) 1899 (470 kg load)

Drag coefficient 0.30

Frontal area 2.27m2

Rolling resistance coefficient 0.009

Baseline accessory load 800 W elec. + 2900 W A/C

Performance Parameters

Standing acceleration 0-60 mph in 8.0 s

Passing acceleration 40-60 mph in 5.3 s

Top speed 110 mph

Gradeability 6.5% at 55 mph at GVM with 2/3 fuel converter power

Vehicle attributes

Engine power 121 kW

Fuel economy 22.2 / 35.2 / 26.6 mpg (urban / highway / composite, unadjusted) Under review/recommendation by VSATT 11

Baseline Vehicle Characteristics – Midsize SUV

MIDSIZE SUV (AUTOMATIC 4WD)

Platform Parameters

Glider Mass 1280 kg

Curb Mass 2015 kg

Test Mass 2151 kg (136 kg load)

Gross Vehicle Mass (GVM) 2667 (516 kg load)

Drag coefficient 0.41

Frontal area 2.88m2

Rolling resistance coefficient 0.009

Baseline accessory load 1200 W elec. + 4000 W A/C

Performance Parameters

Standing acceleration 0-60 mph in 8.4 s

Passing acceleration 40-60 mph in 5.3 s

Top speed 110 mph

Gradeability 6.5% at 55 mph at GVM with 2/3 fuel converter power

Vehicle attributes

Engine power 178 kW

Fuel economy 15.6 / 24.0 / 18.5 mpg (urban / highway / composite, unadjusted) Under review/recommendation by VSATT 12

88 Battery Definition as Key Input to Simulation

Input parameters that define the battery in BLUE

mass compounding Benefit of PHEV range plugging-in

kWh/mi kWh usable (from simulation)

SOC window kWh total Total MPG Benefit

P/E ratio kWmotor Benefit of DOH Performance hybridization constraints kWengine

DOH = degree of hybridization 13

Battery SOC Design Window

In setting requirements, we must have realistic expectations for battery SOC design window because of the impacts on total battery size and life.

SOC design window is a key kWh kWh = useable factor in battery sizing: total SOC window

Battery SOC swing is a strong Battery cycle life is a strong function of driving habits: function of SOC swing:

Daily Mileage Probability Distribution

25%

20% )

% 15% ( y ilit b a b o 10% Pr

0% 0 102030405060708090100 Daily mileage (<= mi)

89 Battery SOC Design Window

In this hypothetical example, all PHEVs use the same SOC design window. However, because of the daily mileage distribution, the low range PHEV batteries experience more-severe cycling and are likely to fail sooner.

100%

90%

80% Average daily SOC 70% swing based on daily mileage distribution 60% Design SOC window based on PHEVx 50%

40%

30%

20%

10% Daily mileage probability distribution 0% 0 102030405060 Daily Mileage / PHEVx

Battery SOC Design Window (cont.)

In this hypothetical example, PHEVs use a different SOC design window based on their PHEVx design range. This produces consistent cycling across the range of PHEVs resulting in consistent expectations for battery life.

100%

90%

80% Average daily SOC 70% swing based on daily Design SOC window mileage distribution 60% based on PHEVx

50%

40%

30%

20%

10% Daily mileage probability distribution 0% 0 102030405060 Daily Mileage / PHEVx

90 Battery Models (Scaleable)

Battery Design Functions Battery Module Cost Functions

2000 1200 NiMH 1800 LI-ION Near Term 1000 1600 Long Term ) ) g

1400 h /k 800 /kW (W 1200 r e w

o 1000 ost ($ 600 P 800 ific c dule C e o p 600 400 S M 400 200 200

0 0 0 50 100 150 200 0 5 10 15 20 25 30 Specific Energy (Wh/kg) Power-to-Energy (P/E) Ratio

Battery Efficiency Curve

100% 95%

) 90% % 85% cy ( 80% en i c

i 75% f f

E 70% 65% 60% 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 P / Pmax

Results: All-Electric Range

70 Midsize Sedans

60 Charge Depleting Range All-Electric Range 50 These results include blended and all-electric PHEVs 40 s e l Mi 30

0 PHEV2 PHEV5 PHEV10 PHEV20 PHEV30 PHEV40 PHEV50 PHEV60 18

91 Results: Useable Battery Energy

PHEV2 35

PHEV5 30 ) e Midsize SUVs: ~515 Wh/mi PHEV10

eabl 25 s

PHEV20 Wh u k ( 20 gy r

ne PHEV30 E y 15 er t t a

B PHEV40

PHEV50 Midsize Sedans: ~290 Wh/mi 5

PHEV60

0 0 10203040506070 PHEVx

Results: Battery Specifications

Midsize Sedans 250 20 10 5 PHEV2

200 PHEV5

PHEV10 )

W 150 k (

r PHEV20 e w Po y r 2 e PHEV30 t t 100 Ba

PHEV40

1 50 PHEV50

PHEV60 0 0 5 10 15 20 25 30 35 40 45 50 Battery Energy (kWh total)

92 Results: Battery Specifications

Midsize SUVs 250 20 10 5 PHEV2

200 PHEV5

PHEV10 )

W 150 k (

r PHEV20 e w Po y r 2

e PHEV30 t t 100 Ba

PHEV40

1 50 PHEV50

PHEV60

0 0 5 10 15 20 25 30 35 40 45 50 Battery Energy (kWh total)

PHEV Battery Options

ESS Technology Comparison - P/E Ratios

1000 500 200 100 50 20 2000

1800 SAFT VLP 7

1600

1400 SAFT VLP 20 Varta Ni UHP

) Kokam SLPB100216216 g PEVE 7.5Ah

/k 1200

W SAFT VLP 30 Kokam SLPB78216216 ( r Cobasys 1000 e Kokam SLPB60460330 w 1000 o SAFT VL30P module SAFT VLM 27 Kokam SLPB55205130

p Kokam SLPB41205130 ic f i Varta Li 6Ah Kokam SLPB80460330 c e 800 SAFT VLM 41 Kokam SLPB30205130 p 5

93 Results: Power-to-Energy Ratio

100

PHEV2

Midsize Sedans and Midsize SUVs PHEV5

PHEV10 ) al

ot PHEV20 t o ( i t 10 Ra

E PHEV30 / P

PHEV40

PHEV50

PHEV60

1 0 10203040506070 PHEVx

Results: Midsize Sedans

Vehicle Curb Mass Vehicle Cost Engine Motor DOH P/E Battery SOC Fuel Use Elec. Use (kg) (kW) (kW) (%) (1/h) (kWh) (%) (mpg) (Wh/mi) CV --- 1429 $ 23,392 121.7 ------26.6 --- HEV downsized 1416 $ 26,790 75.0 38.7 34% 39.0 1.4 37% 36.0 0.0 PHEV10 blended 1494 $ 29,071 100.1 19.1 16% 4.6 6.6 41% 38.3 52.0 PHEV10 UDDS AER 1483 $ 29,698 77.0 41.8 35% 9.1 6.6 41% 42.3 51.9 PHEV10 US06 AER 1584 $ 32,862 80.2 93.8 54% 17.5 7.3 41% 38.7 57.4 PHEV20 blended 1542 $ 31,238 102.8 19.7 16% 2.6 11.9 47% 44.0 95.9 PHEV20 UDDS AER 1536 $ 31,885 78.9 43.1 35% 5.2 11.9 47% 50.1 95.1 PHEV20 US06 AER 1657 $ 35,458 82.5 97.8 54% 10.1 13.3 47% 45.7 104.4 PHEV40 blended 1605 $ 34,237 105.9 21.0 17% 1.7 19.1 59% 55.3 160.5 PHEV40 UDDS AER 1604 $ 34,970 81.0 45.9 36% 3.4 19.2 59% 69.0 157.3 PHEV40 US06 AER 1750 $ 39,095 85.4 102.9 55% 6.5 21.6 59% 63.2 173.4

projected battery costs

94 Results: Midsize SUVs

Vehicle Curb Mass Vehicle Cost Engine Motor DOH P/E Battery SOC Fuel Use Elec. Use (kg) (kW) (kW) (%) (1/h) (kWh) (%) (mpg) (Wh/mi) CV --- 2015 $ 29,400 184.0 ------18.5 --- HEV downsized 2002 $ 33,175 129.2 41.7 24% 25.7 2.4 37% 23.4 0.0 PHEV10 blended 2111 $ 37,198 148.9 30.1 17% 4.2 11.3 41% 25.8 89.3 PHEV10 UDDS AER 2138 $ 38,743 129.9 66.7 34% 8.3 11.4 41% 27.1 90.3 PHEV10 US06 AER 2296 $ 43,905 130.8 152.3 54% 16.2 12.8 41% 25.4 100.4 PHEV20 blended 2194 $ 40,944 153.7 31.6 17% 2.4 20.3 47% 29.3 165.2 PHEV20 UDDS AER 2224 $ 42,528 130.4 69.8 35% 4.8 20.6 47% 32.3 165.9 PHEV20 US06 AER 2415 $ 48,378 132.3 158.7 55% 9.3 23.3 47% 30.3 183.1 PHEV40 blended 2303 $ 46,170 160.0 33.4 17% 1.6 33.0 59% 36.9 279.5 PHEV40 UDDS AER 2334 $ 47,787 131.0 73.5 36% 3.1 33.5 59% 44.5 276.6 PHEV40 US06 AER 2586 $ 55,058 138.4 171.2 55% 6.1 38.2 59% 41.1 305.9

projected battery costs

Results: Curb Mass

Reduction in Fuel Consumption vs Curb Mass

2800 CV

2600 HEV0 Midsize SUVs PHEV2 2400 PHEV5 )

kg 2200 PHEV10 ss ( a M

b PHEV20 r

u 2000 C PHEV30

1800 PHEV40 Midsize Sedans 1600 PHEV50

PHEV60 1400 0 100 200 300 400 500 600 Reduction in Annual Fuel Consumption (Gals.)

95 Results: Powertrain Volume

Reduction in Fuel Consumption vs Powertrain Volume

900 CV

800 HEV0 Midsize SUVs PHEV2 700

) PHEV5 (L me

u 600 l PHEV10 o V n i

ra PHEV20

rt 500 e w o

P PHEV30

400 PHEV40 Midsize Sedans 300 PHEV50

PHEV60 200 0 100 200 300 400 500 600 Reduction in Annual Fuel Consumption (Gals.)

Results: Battery Volume

Reduction in Fuel Consumption vs Battery Volume

300 HEV0

250 PHEV2

Midsize SUVs PHEV5 200 ) L PHEV10 ( me u l 150 PHEV20 Vo y r e t t PHEV30 Ba 100 PHEV40 Midsize Sedans

50 PHEV50

PHEV60 0 0 100 200 300 400 500 600 Reduction in Annual Fuel Consumption (Gals.)

96 Results: Vehicle Cost

Reduction in Fuel Consumption vs Vehicle Cost

$60,000 CV

$55,000 Midsize SUVs HEV0 projected battery costs $50,000 PHEV2 ) ) $ $ PHEV5 (

t $45,000 st ( s o

Co PHEV10 l il C i a ta t $40,000 e R Re PHEV20 e e l cl i h hic e e $35,000 V V PHEV30

$30,000 PHEV40 Midsize Sedans

projected battery costs PHEV50 $25,000

PHEV60 $20,000 0 100 200 300 400 500 600 Reduction in Annual Fuel Consumption (Gals.)

Results: Vehicle Cost

Reductition in Fuel Consumptption vs Vehicle Cost

$60,000 CV

$55,000 HEV0

$50,000 PHEV2 Midsize Sedans ) )

$ $ current battery costs PHEV5 ( (

t t $45,000 s s

Co Co PHEV10 l l i i a a t t $40,000

Re Re PHEV20 e e l l hic hic

e e $35,000

V V PHEV30

$30,000 PHEV40 Midsize Sedans

projected battery costs PHEV50 $25,000

PHEV60 $20,000 0 100100 200 300 400 500 600 Reducttiion in Annual Fuueel Consumptptiion ((GGals.)

97 PHEVs Reduce Fuel Consumption By >50% On Real- World Driving Cycles

227 vehicles from St. Louis each modeled as a conventional, hybrid and PHEV

40 400 Conventional Average Daily Costs Hybrid 26 mpg ) 35 350 ) Gas. Elec. ¢/mi

% PHEV20

( PHEV40 e CV $3.45 --- 9.1 lons l s

30 300 a U g HEV $2.48 --- 6.5 n ( I d t 37 mpg e 25 • 8647 total miles driven 250 PHEV20 $1.58 $0.48 5.4 e l

• 100% replacement of ume F

s PHEV40 $1.21 $0.72 5.1 e sample fleet cl 20 200 i 58 mpg & h Con

l Assumes $2.41/gal and 9¢/kWh e 140 Wh/mi e V f 15 150 Fu o e e v i g t a PHEVs: t 10 100 la u en 76 mpg & >40% reduction in c m r 5 211 Wh/mi 50 energy costs Cu Pe >$500 annual savings 0 0 0 5 10 15 20 25 30 Time of Day (hr) Source: Tony Markel, Jeff Gondor, and Andrew Simpson (NREL), 31 Milestone Report , Golden, CO, May 2006

Fuel Economy and All Electric Range Comparison

• Difference between rated (EPA drive cycles) and Real median values are significant for the PHEVs — Consumers likely to observe fuel economy higher than rated value in typical driving — Vehicles designed with all electric range likely to operate in a blended mode to meet driver demands

Fuel Economy (mpg) ** All Electric Range (mi) Rated Median Rated Median Conventional 26 24.4 n/a n/a HEV 39.2 35.8 n/a n/a PHEV20 54 70.2 22.3 5.6 PHEV40 67.4 133.6 35.8 3.8 ** Fuel economy values do not include electrical energy consumption

Source: Tony Markel, Jeff Gonder, and Andrew Simpson (NREL), 32 Milestone Report , Golden, CO, May 2006

98 Technical Summary

Midsize Sedan Electrical energy use on UDDS: ~290 Wh/mi Battery power for all-electric UDDS capability: ~60kW PHEV 10-40 useable energy: 2.7 – 11.4 kWh PHEV 10-40 total energy: 6.6 – 19.2 kWh P/E ratio (total energy): 9.1 – 3.4

Midsize SUV Electrical energy use on UDDS: ~510 Wh/mi Battery power for all-electric UDDS capability: ~100kW PHEV 10-40 useable energy: 4.7 – 19.9 kWh PHEV 10-40 total energy: 11.4 – 33.5 kWh P/E ratio (total energy): 8.3 – 3.1

Conclusions and Key Messages

In setting energy storage requirements for PHEVs, we need to consider: 1. There is a VERY large spectrum of PHEV designs — Battery energy: 2-50kWh, battery power: 20-250kW 2. There is a link between SOC window, driving habits and cycle life that impacts energy storage size and cost — Different PHEVx requires different SOC design window 3. A wide range of P/E ratios may be needed — P/E ratios = 1-40 4. Tradeoffs between fuel economy / oil displacement and powertrain mass, volume and cost — Blended strategy PHEVs can help reduce battery mass, volume and cost in the near term. — In the long term, all-electric PHEVs achieve a target level of oil displacement at the lowest mass, volume and cost Fundamental Question: What kind of PHEV are we setting requirements for?

99 Section 2.5

Title: “Plug-In Hybrid Modeling and Application: Cost/Benefit Analysis” Type: Presentation Author: Andrew Simpson Date: Aug. 24, 2006 Conference or Meeting: Presented at the Third AVL Summer Conference on Automotive Simulation Technology: Modeling of Advanced Powertrain Systems Abstract: Includes a brief summary of the cost and consumption benefit analysis

100 Plug-in Hybrid Modeling and Application: Cost / Benefit Analysis

Presented at the 3rd AVL Summer Conference on Automotive Simulation Technology: Modeling of Advanced Powertrain Systems

Andrew Simpson National Renewable Energy Laboratory

Thursday, 24th August 2006

With support from the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program

Disclaimer and Government License

This work has been authored by Midwest Research Institute (MRI) under Contract No. DE-AC36- 99GO10337 with the U.S. Department of Energy (the “DOE”). The United States Government (the “Government”) retains and the publisher, by accepting the work for publication, acknowledges that the Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for Government purposes.

Neither MRI, the DOE, the Government, nor any other agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe any privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the Government or any agency thereof. The views and opinions of the authors and/or presenters expressed herein do not necessarily state or reflect those of MRI, the DOE, the Government, or any agency thereof.

101 Presentation Outline

• What is a plug-in hybrid-electric vehicle (PHEV)? • Potential petroleum reduction from PHEVs • Simulation of PHEV efficiency and cost — Baseline vehicle assumptions — Powertrain technology scenarios — Components models (cost, mass, efficiency) • Results — Component sizing — Fuel Economy — Incremental cost — Payback scenarios • Conclusions & Next Steps

A Plug-In Hybrid-Electric Vehicle (PHEV)

Fuel Flexibility BATTERY RECHARGE

REGENERATIVE BRAKING

PETROLEUM

AND/OR

ELECTRICITY ELECTRIC ACCESSORIES

ADVANCED ENGINE ENGINE IDLE-OFF

ENGINE DOWNSIZING 4

102 Some PHEV Definitions

All-Electric Range (AER): After a full recharge, the total miles driven electrically (engine-off) before the engine turns on for the first time.

Blended Mode: A charge-depleting operating mode in which the engine is used to supplement battery/motor power.

PHEV20: A PHEV with useable energy storage equivalent to 20 miles of driving energy on a reference driving cycle.

NOTE: PHEV20 does not imply that the vehicle will achieve 20 miles of AER on the reference cycle nor any other driving cycle. Operating characteristics depend on the power ratings of components, the powertrain control strategy and the nature of the driving cycle

PHEV Key Benefits and Challenges

KEY BENEFITS KEY CHALLENGES

Consumer: • Lower “fuel” costs • Recharging locations • Fewer fill-ups • Home recharging convenience • Battery life • Fuel flexibility ? • Component packaging Nation: • Less petroleum use • Vehicle cost • Less greenhouse and regulated emissions • Energy diversity/security Cost-Benefit Analysis

103 National Driving Statistics: 1995 National Personal Transportation Survey

Daily Mileage Distribution and Utility Factor Curve 100 Daily mileage distribution 90 Utility Factor curve 80

) 60 (% y t i l i 50 ab ob r 40 P

0 0 20 40 60 80 100 Daily Mileage (mi)

Potential Petroleum Reduction from PHEVs

) 100% Battery energy (% 90% on i

pt 80% m u s

n 70%

Co PHEV60 60% m

eu PHEV40 ol 50% Challenging for r Prius (Corolla)

et HEV technology 40%

n P Escape Civic 30% PHEV20 on i i t Accord c 20% Highlander WHAT ARE THE Vue RELATIVE COSTS? Redu 10% al HEV Battery power

Tot 0% 0% 20% 40% 60% 80% 100% Reduction in Charge-Sustaining Mode Petroleum Consumption (%)

104 PHEV Efficiency and Cost Model

70 100% engine 60 motor 90% Vehicle Configurations SOC 50 80%

conventional automatic 40 70%

) 30 60% )

pre-transmission parallel hybrid: W k % ( ( r

20 50% C e

HEV or PHEV w SO

Po 10 40%

2 technology scenarios 0 30% – near term and long term -10 20% -20 10%

-30 0% Approach 0 5 10 15 20 25 30 35 40 Distance (mi) Dynamic, power-flow simulation Calculates component sizes and costs Iterative mass-compounding Measures fuel/electricity consumption using NREL-proposed revisions to SAE J1711 Battery definition is key input to the simulation

Baseline Vehicle Characteristics – Midsize Sedan

MIDSIZE SEDAN (AUTOMATIC)

Platform Parameters

Glider Mass 905 kg

Curb Mass 1429 kg

Test Mass 1565 kg (136 kg load)

Gross Vehicle Mass (GVM) 1899 (470 kg load)

Drag coefficient 0.30

Frontal area 2.27m2

Rolling resistance coefficient 0.009

Baseline accessory load 800 W elec. + 2900 W A/C

Performance Parameters

Standing acceleration 0-60 mph in 8.0 s

Passing acceleration 40-60 mph in 5.3 s

Top speed 110 mph

Gradeability 6.5% at 55 mph at GVM with 2/3 fuel converter power

Vehicle attributes

Engine power 121 kW

Fuel economy 22.2 / 35.2 / 26.6 mpg (urban / highway / composite, unadjusted)

105 Powertrain Technology Scenarios

Battery Near-Term Scenario Long-Term Scenario Chemistry NiMH Li-Ion Module cost Double EPRI projections, see slide 12 EPRI projections, see slide 12 Packaging cost EPRI Same Module mass NiMH battery design function (Delucchi), see slide 12 Li-Ion battery design function (Delucchi), see slide 12 Packaging mass Delucchi Same

Efficiency Scaleable model based on P/E ratio Same SOC window SOC design curve based on JCI data Same for NiMH cycle-life, see slide 11 (assumes Li-Ion achieves same cycle life as NiMH)

Motor Near-Term Scenario Long-Term Scenario Mass DOE 2006 current status Based on GM Precept motor drive Efficiency 95% peak efficiency curve Same Cost EPRI (near term) EPRI (long term)

Engine Near-Term Scenario Long-Term Scenario Mass Based on MY2003 production engines Same* Efficiency 35% peak efficiency curve Same* Cost EPRI Same*

* Engine technologies were not improved so as to isolate the benefits of improved plug-in hybrid technology

Battery Definition as Key Input to Simulation

Input parameters that define the battery in BLUE

mass compounding Benefit of PHEV range plugging-in

kWh/mi kWh usable (from simulation)

SOC window kWh total Total MPG Benefit

P/E ratio kWmotor Benefit of DOH Performance hybridization constraints kWengine

DOH = degree of hybridization 12

106 Battery SOC Design Window

Battery SOC design curve for 15 year cycle life

100%

90%

80% Average daily SOC 70% swing based on daily Design SOC window mileage distribution 60% based on PHEVx

50%

40%

30%

20%

10% Daily mileage probability distribution 0% 0 102030405060 Daily Mileage / PHEVx

Battery Models (Scaleable)

Battery Design Functions Battery Cost Functions

50 20 10 2000 1200 NiMH (near-term scenario) 1800 LI-ION (long-term scenario) NiMH (near-term) 1000 1600 Li-Ion (long-term) h) W

1400 k g)

k 800 $/ / ( t W 1200 ( os er C c

5 i ow

1000 f 600 i P c i pec f i 800 e S pec 400 S 600 odul M 400 2 200 200

0 0 0 20 40 60 80 100 120 140 160 180 200 0 5 10 15 20 25 30 Specific Energy (Wh/kg) Power-to-Energy Ratio (1/h)

107 Results: Battery Specifications

Battery Power vs Energy for PHEVs Long-term scenario Midsize Sedans 20 10 6 4 120 PHEV2

100 PHEV5

PHEV10 80 )

W UDDS (k

r all-electric PHEV20 e w

o 60 2 P y r PHEV30 e t t a B 40 UDDS PHEV40 blended 1 PHEV50 20

PHEV60 0 0 5 10 15 20 25 30 Total Battery Energy (kWh)

Results: Battery Specifications

Reduction in Fuel Consumption vs Powertrain Cost Increment - Midsize Sedans

$20,000 HEV0 Long-term scenario $18,000 LI-ION BATTERIES PHEV2 $16,000 PHEV5

t $14,000 en PHEV10 em

cr $12,000

n PHEV20 st I

o $10,000

C PHEV30 n i a r

t $8,000

er PHEV40 w o $6,000 P PHEV50 $4,000 PHEV60 $2,000 UDDS AER vehicles $- 0 100 200 300 400 500 Reduction in Annual Fuel Consumption (gals.)

108 PHEV Energy Use

PHEV Onboard Energy Use: Near and Long-Term Scenarios

600 10000 27 mpg Near-Term: Petroleum Long-Term: Petroleum 9000 Near-Term: Electricity s) 500 h) l

Long-Term: Electricity 8000 W ga k ( on ( UDDS AER PHEVs on i

7000 i 400 umpt 6000 umpt s ons on

C 300 5000 C y t i um c e i

4000 r t ol r c t e

200 l

3000 E Pe al ual 2000 nnu nn

A 100 A 1000

0 0 Conventional HEV0 PHEV10 PHEV20 PHEV40

Powertrain Costs Comparison – Near Term

Powertrain Costs (incl. retail markups)

UDDS AER PHEVs $30,000 $27,851 Charging Plug $663 Battery $25,000 Motor/Inverter Transmission Engine $21,181 $663 $20,000

$16,386 $19,251 $663 $15,000 $12,889

$10,976 $8,296 $10,000 $3,907

$6,002 $2,677 $2,166 $2,414 $2,516 $5,000 $1,998 $2,000 $2,018 $2,035 $2,057

$4,004 $2,902 $2,995 $3,079 $3,203 $- Conventional HEV0 PHEV10 PHEV20 PHEV40

109 Powertrain Costs Comparison – Long Term

Powertrain Costs (incl. retail markups)

$30,000

Charging Plug Battery Motor/Inverter $25,000 Transmission Engine UDDS AER PHEVs $20,000

$17,249 $663 $14,261 $15,000 $663 $12,111 $663 $9,626 $9,073 $10,000 $6,740 $4,677 $2,523 $6,002 $1,680 $1,842 $1,882 $1,924 $5,000 $1,998 $1,994 $2,005 $2,012 $2,022

$4,004 $2,876 $2,925 $2,964 $3,013 $- Conventional HEV0 PHEV10 PHEV20 PHEV40

Overall Cost Comparison for HEVs and PHEVs

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$60,000 Near-term scenario NIMH BATTERIES $50,000

$40,000 st Co ve

i PHEV40 t $30,000 a l

u PHEV20 m

u PHEV10 C $20,000 HEV0 $3.00 / gal. (today) CV $0.09¢/kWh (2005 average) $10,000 Maintenance costs not included, no discount rate applied $- 0 5 10 15 Years after purchase

110 Overall Cost Comparison for HEVs and PHEVs

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$60,000 Long-term scenario LI-ION BATTERIES $50,000

$40,000 st Co ve

i PHEV40 t $30,000 a l

u PHEV20 m

u PHEV10 C $20,000 HEV0 $3.00 / gal. (today) CV $0.09¢/kWh (2005 average) $10,000 Maintenance costs not included, no discount rate applied $- 0 5 10 15 Years after purchase

Overall Cost Comparison for HEVs and PHEVs

Cumulative Vehicle plus Energy (Fuel/Elec.) Costs

$60,000 Long-term scenario LI-ION BATTERIES $50,000

$40,000 st Co ve

i PHEV40 t $30,000 a l

u PHEV20 m

u PHEV10 C $20,000 HEV0 $5.00 / gal. (day after tomorrow??) CV $0.09¢/kWh (2005 average) $10,000 Maintenance costs not included, no discount rate applied $- 0 5 10 15 Years after purchase

111 Vehicle Costs cont.

Why might PHEV buyers pay more?

1. Tax incentives

Conclusions

1. There is a very broad spectrum of HEV-PHEV designs.

2. Key factors in the HEV/PHEV cost-benefit equation include: • Battery costs • Fuel costs • Control strategy (particularly battery SOC window) • Driving habits (annual VMT and trip-length distribution)

3. Based on the assumptions of this study: • HEVs can reduce per-vehicle fuel use by approx. 30%. • PHEVs can reduce per-vehicle fuel use by up to 50% for PHEV20s and 65% for PHEV40s. • In the long term, powertrain cost increments are predicted to be $2-6k for HEVs, $7-11k for PHEV20s and $11-15k for PHEV40s assuming that projected component (battery) costs can be achieved. • Note this study did not consider benefits from platform engineering (i.e. mass/drag reduction).

112 Conclusions (cont.)

4. Based on overall costs (powertrain plus energy): • At today’s fuel and powertrain component costs, conventional vehicles are the most cost-competitive. • HEVs become the most cost-competitive EITHER if fuel prices increase OR projected battery costs are achieved. • PHEVs become cost-competitive ONLY if projected battery costs are achieved AND fuel prices increase. • Tax incentives and/or alternative business models (e.g. battery lease) may be required for successful marketing of PHEVs

Next Steps

• Present this work at EVS22

• Expand the HEV-PHEV analysis space to include: — Platform engineering (mass/drag reduction) — Different performance constraints / component sizes SAE 2007 paper • Detailed simulation of promising PHEV designs: — Real world driving patterns (e.g. St Louis data) — Control strategy optimization TRB 2007 paper • Optimization of PHEV market competitiveness using Technical Targets Tool Ongoing analysis

113 Section 3

Conclusions from the travel survey data are:

• Approximately 50% of the vehicles traveled less than 29 miles a day. A PHEV with 20–30 miles of electric range capability provides sufficient energy to displace a large percentage of daily petroleum consumption. Because many vehicles drive less than 30 miles a day, the battery of a PHEV with 30 or more miles of electric range capability would likely be under-utilized on a daily basis. • The travel survey data demonstrated that there is a broad spectrum of driving behavior, varying from short to long distances and from mild to aggressive driving intensities. The Urban Dynamometer Driving Schedule and Highway Fuel Economy Test driving profiles used for fuel economy reporting today fall short of capturing the typical driving behavior of today’s consumer. • Contrary to experience with hybrid electric vehicles, which typically deliver fuel economies significantly less than their rated values, simulations of real-world driving suggest that a large percentage of drivers of PHEVs will likely observe fuel economies in excess of the rated fuel economy values. However, because of high power requirements in real-world cycles, drivers are unlikely to experience significant all-electric operation if PHEVs are designed for all-electric range on the Urban Dynamometer Driving Schedule. • If all vehicles in the travel survey “fleet” were PHEV20 vehicles designed for all-electric range on the Urban Dynamometer Driving Schedule, petroleum consumption would be reduced by 56% relative to a conventional vehicle fleet. The PHEV40 reduced consumption by an additional 12% and was equivalent to ≈1 gal/vehicle/day of petroleum savings. Including electricity costs, the average annual fuel costs savings for the “fleet” of PHEVs is more than $500/vehicle/year. • The time-of-day usage pattern obtained from global positioning system (GPS) travel survey data and the recharge requirements from simulation will be extremely valuable for determining the impact of PHEV recharging scenarios on the electric utility grid.

Since the St. Louis analyses were completed, data from five other metropolitan GPS travel surveys have been obtained. The driving profile database will expand from 227 to more than 2,000 vehicles. Additional analyses will be completed using the full collection of more than 2,000 driving profiles. Real-world travel simulations will be executed to consider variations in platform, aerodynamics, performance, control, and recharge scenarios. In addition, the database will be used to explore the emissions control implications of potential engine cold-starts and the fuel consumption impacts of location-specific air conditioning use.

For more extensive discussion of this topic, please refer to sections 3.1, 3.2, and 3.3.

114 Section 3.1

Title: “Plug-In Hybrid Electric Vehicles: Current Status, Long-Term Prospects, and Key Challenges” Type: Presentation Author: Tony Markel Date: May 8, 2006 Conference or Meeting: Clean Cities Congress and Exposition in Phoenix, Arizona Abstract: Discusses what a plug-in hybrid is, its potential benefits, and the key technical challenges to overcome

115 Plug-in Hybrid Electric Vehicles Current Status, Long-Term Prospects and Key Challenges

Presented at Clean Cities Congress and Expo by Tony Markel National Renewable Energy Laboratory

May 8th, 2006

With support from the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program

Presented at Clean Cities Congress and Expo held May 7-10, 2006 in Phoenix, Arizona NREL/PR-540-40239

116 The Perfect Storm

Domestic Production Domestic Consumption 20

• Petroleum consumption has steadily ) y a d / increased while domestic production has b 15 mm

continued to decline m ( u

3 e n li

aso 2.5 e G al) /g ag $ r 2 e e ( v ic A r l

a Source: Hubbert Center Newsletter #99/1 R. Udall and S. Andrews n

il P 1.5 o a i t a Ret N 1 ekly

e WHAT’S OUR PLAN? W 0.5

Source: U.S. Department of Energy, Energy Information Administration 0 3 Aug 11, May 07, Jan 31, Oct 28, Jul 24, Apr 19, Jan 14, Oct 10, Jul 06, 1987 1990 1993 1995 1998 2001 2004 2006 2009

A “Full” Hybrid

ADVANCED ENGINE

ELECTRIC ACCESSORIES

ENGINE DOWNSIZING

PETROLEUM ONLY

REGENERATIVE BRAKING ENGINE IDLE-OFF

76hp gasoline engine, 67hp electric motor, 1.5kWh battery 4

117 A Plug-In Hybrid

ADVANCED ENGINE Fuel Flexibility

ENGINE DOWNSIZING ELECTRIC ACCESSORIES

PETROLEUM

AND/OR

ELECTRICITY

ENGINE IDLE-OFF REGENERATIVE BRAKING BATTERY RECHARGE

76hp gasoline engine, 67hp electric motor, 9.0kWh battery (30mi) 5

Oil Use Reduction with HEVs

Light Duty Fleet Oil Use - Impact of HEVs on Consumption

16 2005 AEO Base Case HEV Scenario 14

12 Oil use same as today! BD)

P 10 3 MBPD M on ( i

pt 8 um

6 Cons l i O 4 This highly aggressive scenario assumes 100% HEV sales from 2010 onwards… 2

0 2000 2005 2010 2015 2020 2025 2030 Year HEVs unable to reduce consumption below today’s consumption level 6 Produced using VISION model, MBPD = million barrels per day

118 Oil Use Reduction with PHEVs

Light Duty Fleet Oil Use - Impact of PHEVs on Consumption

16 2005 AEO Base Case PHEV Scenario 14

12 Oil use reduction! BD)

P 10 4 MBPD M on ( i

pt 8 um

6 Cons l i

O PHEVs on E85 ?? 4 This highly aggressive scenario assumes 100% HEV sales from 2010 and 50% PHEV40 sales from 2020 onwards… 2

0 2000 2005 2010 2015 2020 2025 2030 Year PHEVs reduce oil consumption with a transition to electricity 7 Produced using VISION model, MBPD = million barrels per day

OEM Plug-In Hybrids

2003 Renault Kangoo Elect’road - up to 50mi electric range - approximately 500 sold in Europe

DaimlerChrysler Sprinter PHEV - 15 prototypes being produced for testing in various locations in Europe and North America - up to 20mi electric range

119 Other PHEV Prototypes - Industry

EnergyCS Plug-In Prius HyMotion Escape PHEV AFS Trinity Extreme Hybrid™

AC Propulsion Jetta PHEV Esoro AG H301

Design Options All-Electric vs Blended Strategy All-Electric

70 100% engine • Engine turns on when battery 60 motor 90% SOC reaches low state of charge 50 80% • Requires high power battery and 40 70%

) 30 60% motor ) W k % ( 20 50% ( wer SOC

Po 10 40%

0 30% -10 20% Blended

-20 10% 70 100% engine -30 0% 60 motor 90% 0 5 10 15 20 25 30 35 40 SOC 50 80% Distance (mi) 40 70%

) 30 60% ) W k % ( r

20 50% C ( e

• Engine turns on when power SO Pow 10 40%

exceeds battery power capability 0 30% • Engine only provides load that -10 20% exceeds battery power capability -20 10% -30 0% 0 5 10 15 20 25 30 35 40 Distance (mi) 10

120 Household Travel Survey Data Can be Used to Predict Real-World Benefits of Advanced Technologies • Provides valuable insight into travel behavior • GPS augmented surveys supply details needed for vehicle simulation

PHEVs Reduce Fuel Consumption By 50% On Real- World Driving Cycles

227 vehicles from St. Louis each modeled as a conventional, hybrid and PHEV

40 400 Conventional Average Daily Costs Hybrid 26 mpg ) 35 350 ) Gas. Elec. ¢/mi

% PHEV20

( PHEV40 e CV $3.15 --- 8.3 lons l s

30 300 a U g HEV $2.21 --- 5.8 n ( I d t 37 mpg e 25 • 8647 total miles driven 250 PHEV20 $1.41 $0.48 5.0 e l

• 100% replacement of ume F

s PHEV40 $1.08 $0.72 4.7 e sample fleet cl 20 200 i 58 mpg & h Con

l Assumes $2.15/gal and 9¢/kWh e 140 Wh/mi e V f 15 150 Fu o e e v i g t a PHEVs: t 10 100 la u en 76 mpg & ~40% reduction in c m r 5 211 Wh/mi 50 operating costs Cu Pe ~$460 annual savings 0 0 0 5 10 15 20 25 30 Time of Day (hr)

121 HEVs and PHEVs Likely to Reduce Greenhouse Emissions

13 Source: Hybrid Electric Vehicle Working Group, www.epri.com

Electrified Miles May Lead to Cleaner Operation

14 Source: Hybrid Electric Vehicle Working Group, www.epri.com

122 In-Use Simulations Show Reasonable Recharge Times with Standard Household Outlet

• Typical vehicle is used less than 5% of the time — Lots of opportunity for recharging • Both PHEV20 and PHEV40 owners likely to get full recharge overnight with standard outlet Vehicle In-Use (red) PHEV40 45

25 40

35 ) ) % hr (

20 t 30 ( e e y a Fl 25 D 15 of of ge 20 a e nt e m 15 c r Ti 10 e P 10

5 5 0 0 50 100 150 200 0 1 2 3 4 5 6 7 8 9 Vehicle ID Recharge Duration @ 110V 15A (hr) 15

Technical Challenges Battery Life

• PHEV battery likely to deep-cycle each day driven: 15 yrs equates to 4000-5000 deep cycles • Also need to consider combination of high and low frequency cycling

70%

50%

4000

Data presented by Christian Rosenkranz (Johnson Controls) at EVS 20 16

123 Technical Challenges Battery Packaging

Technical Challenges Vehicle Costs

100%

90% HOW CAN WE SAVE THE MOST GALLONS AT THE LEAST COST? 80%

70% (%)

st 60% o

50% Prius (Corolla)

ental C PHEVs? m

e 40% Civic cr HEVs n Escape I 30% Highlander 20% Accord 10% Vue 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Gasoline Savings (%) 18

124 Conclusions

125 Section 3.2

Title: “Plug-In Hybrid Vehicle Real-World Performance Expectations” Type: Presentation Author: Tony Markel Date: June 14, 2006 Conference or Meeting: Presented to the Vehicle Systems Analysis Technical Team Abstract: Summarizes the simulated performance of optimal PHEVs on a collection of real-world driving profiles from the St. Louis, Missouri, metropolitan area

126 Plug-in Hybrid Vehicle Real-world Performance Expectations

a presentation to FreedomCAR Vehicle Systems Analysis Tech Team by Tony Markel National Renewable Energy Laboratory

June 14, 2006

With support from the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program

127 Outline

• Objective • Approach • Assumptions • Travel survey data • Simulated in-use operation • Conclusions • Next steps

Objective and Justification

• Extend preliminary PHEV analyses to predict consumption and performance of vehicles in real- world applications

• Fuel consumption benefits and operation of PHEVs are highly dependent on usage pattern — What are real usage patterns like? — How do rated versus in-use fuel consumption and all electric range compare?

128 Approach

• Consider PHEV20 and PHEV40 architectures — Based on Presidential initiatives and recent cost/benefit analysis — Component sizing and energy management strategy to provide all electric operation on urban profile • Generate a collection of real-world driving profiles from St. Louis, Missouri metropolitan household travel survey data — Sample data from GPS units in support of larger written survey • Optimize vehicle energy management strategy parameters — Vary electric launch speeds at high and low SOC set points and charge torque — Minimize fuel consumption on UDDS, US06, HWFET, SC03 — Minimize the number of engine start/stop events • Complete detailed cycle simulation — Use existing PHEV, HEV, and Conventional architecture models — 24 hour driving profiles » ~10 minutes per profile * 227 profiles = 38 hours (for one architecture) — Include 4 architectures (Conventional, HEV, PHEV20, PHEV40) • Summarize simulation results

Baseline Vehicle Characteristics

MIDSIZE SEDAN (AUTOMATIC) – MSRP $23,400

Platform Parameters

Glider Mass 905 kg

Curb Mass 1429 kg

Test Mass 1565 kg (136 kg load)

Gross Vehicle Mass (GVM) 1899 (470 kg load)

Drag coefficient 0.30

Frontal area 2.27m2

Rolling resistance coefficient 0.009

Accessory load 700 W elec. or 823 W mech.

Performance Parameters

Standing acceleration 0-60 mph in 8.0 s

Passing acceleration 40-60 mph in 5.3 s

Top speed 110 mph

Gradeability 6.5% at 55 mph at GVM with 2/3 fuel converter power

Vehicle attributes

Engine power 121 kW

Fuel economy 22.2 / 35.2 / 26.6 mpg (urban / highway / composite, unadjusted)

129 PHEV20 and PHEV40 Characteristics

Attribute Units PHEV20 PHEV40 Conventional HEV Curb Mass kg 1488 1567 1429 1399 Battery Power kW 47.0 51.8 n/a 50 Battery Energy kWh 9.4 18.5 n/a 1.9 Battery Power to Energy Ratio -- 52.8 n/a 26.3 Motor Power kW 43.6 48.0 n/a 39 Engine Power kW 79.4 81.9 121.7 82 DOH % 35.4 37.0 n/a 32.2

• Additional assumptions — 70% usable SOC window (stretch goal) — Lithium ion technology

Household Travel Survey Data

• Provides valuable insight into travel behavior • GPS augmented surveys supply details needed for vehicle simulation

130 St. Louis Travel Data Analysis Daily Driving Distance Similar to 1995 NPTS Data

St. Louis HHTS Data 1995 NPTS Data 7 100 12 100

90 90 6 10 )

80 ) 80 % y (% ( 5 70 70 nc )

) Frequency (%) 8 e

% Cumulative Freq. (%) 60 60 y (%

4 equ equency nc

6 50 Fr

50 e Fr uency ( ive q t 3 ve 40 equ i e 40 at Fr l Fr 4 ula

u 30 30 m

2 m u

20 um C

20 2 C 1 10 ~29 mi 10 ~33 mi 0 0 0 0 0 5 0 0 5 5 0 0 5 0 0 1 15 2 25 30 3 40 4 5 55 6 65 70 7 80 85 9 95 2 4 6 8 2 8 4 2 2 8 8 6 0 0------10 10 12 14 16 18 20 2 24 26 28 30 32 34 36 3 40 42 44 46 48 50 52 5 56 58 60 6 64 66 68 70 7 74 76 7 80 82 84 86 8 90 92 94 9 98 5 0- 5- 5- 0- 5- 0- 0- 0- 10 1 1 20- 2 30 35 4 45 50- 55 60 6 7 75- 8 85 9 5- Daily Travel Distance (miles) 9 Daily Distance (mi)

• St. Louis data set includes 227 vehicles from 147 households • Complete second by second driving profile for one day • 8650 miles of travel • St. Louis data set is a small sample of real data • NPTS data is generated from mileage estimates

In-use PHEV Analysis

• 227 unique driving profiles • 4 vehicle architectures — Conventional, HEV, PHEV20, and PHEV40

• Expectations — PHEV20 should displace ~50% of the conventional fleet consumption — PHEVs will typically see fuel economy in excess of the rated value — There is a correlation between PHEV benefit and daily driving distance — All electric range will be less in real driving profiles than on certification cycles 10

131 List of Results

• Fleet consumption summary • Distribution of fuel economy • Distribution of AER • Distribution of EV miles • Distribution of charge depleting miles fraction of total miles • Distribution of charge sustaining miles fraction of total miles • Consumption comparison as a function of daily distance

PHEVs Reduce Fuel Consumption By >50% On Real- World Driving Cycles

227 vehicles from St. Louis each modeled as a conventional, hybrid and PHEV

40 400 Conventional Average Daily Costs Hybrid 26 mpg ) 35 350 ) Gas. Elec. ¢/mi

% PHEV20

( PHEV40 e CV $3.45 --- 9.1 lons l s

30 300 a U g HEV $2.48 --- 6.5 n ( I d t 37 mpg e 25 • 8647 total miles driven 250 PHEV20 $1.58 $0.48 5.4 e l

• 100% replacement of ume F

s PHEV40 $1.21 $0.72 5.1 e sample fleet cl 20 200 i 58 mpg & h Con

132 Fuel Economy DistributionHEV - Details Conventional 20 16 18 59.5% less than mean 14 65.6% less than mean 16 12 14 )

) 10 12 % % ( y cy (

n 10

8 e u q e r equenc 8 r F

F 6 6 4 4

2 2

0 0 5 10 15 20 25 30 35 15 20 25 30 35 40 45 Fuel Economy (mpg) Fuel Economy (mpg)

PHEV20 PHEV40 25 30

32.2% less than mean 26.4% less than mean 20 25

20 )

15 ) % % ( ( y y c c 15 equen r 10 equen r F F 10

5 5

13 0 0 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Fuel Economy (mpg) Fuel Economy (mpg)

All Electric Range Distribution

PHEV20 PHEV40 30 40

35 25

%) 20 %)

t ( t ( 25 e e e e l l f F f F

o 15 o 20 e e g g ta ta n n 15 ce ce r 10 r Pe Pe 10

5 5

0 0 1 3 5 7 9 11 13 15 17 19 21 23 25 0 5 10 15 20 25 30 35 40 All Electric Range (mi) All Electric Range (mi)

• Vehicles were designed to operate all electrically on UDDS • Power demands of real profiles exceed UDDS peak power within the first few miles

133 Fuel Economy and All Electric Range Comparison

• Difference between rated and median values are significant for the PHEVs — Consumers likely to observe fuel economy higher than rated value in typical driving — Vehicles designed with all electric range likely to operate in a blended mode to meet driver demands

Fuel Economy (mpg) ** All Electric Range (mi) Rated Median Rated Median Conventional 26 24.4 n/a n/a HEV 39.2 35.8 n/a n/a PHEV20 54 70.2 22.3 5.6 PHEV40 67.4 133.6 35.8 3.8

** Fuel economy values do not include electrical energy consumption

Electric Vehicle Miles Distribution

PHEV20 14

12 50% of the PHEV20

10 vehicles had 10 miles or ) % ( less in EV mode eet l 8 F of PHEV40 age 6 18 ent c er P 4 16

14 2 )

% 12 ( t

0 ee

1 3 5 7 9 11 13 15 17 19 21 23 Fl 10 Electric Range (mi)

age of 8 nt e c r

e 6 P Vehicles operate in a 4 blended mode most of 2 0 0 5 10 15 20 25 30 35 40 the time Electric Range (mi)

134 EV Mode Usage

PHEV20 20

) 14 % ( t 12 ee Fl 10

age of PHEV40 nt 8 e 20 c r e

P 6 18

4 16

) 14 % 0 ( 12

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 eet l

Fraction of Miles in EV Mode (--) F

of 10 age

nt 8 e c er

P 6

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Fraction of Miles in EV Mode (--)

Charge Depleting vs.Charge Sustaining Mode Operation – PHEV20 PHEV20 PHEV20 40 40

35 35

30 30 ) ) % %

( 25 ( 25 eet eet l l F F 20 20 ge of ge of a a t t

en 15 en 15 c c er er P P 10 10

5 5

0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 44.1% of 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Fraction of Miles in Charge Depleting Mode (--) VMT in Fraction of Miles in Charge Sustaining Mode (--) PHEV20 PHEV20 30 charge 45 40 25 depleting 35 ) mode ) % 20 % 30 ( ( eet eet l l 25 F F 15 ge of ge of

a a 20 t t en en c c

er 10 er 15 P P

10 5 5

18 0 0 1 3 5 7 9 11 13 15 17 19 21 23 25 0 50 100 150 200 250 Charge Depleting Distance (mi) Charge Sustaining Distance (mi)

135 Charge Depleting vs.Charge Sustaining Mode Operation – PHEV40 PHEV40 PHEV40 70 70

60 60

50 50 ) ) % % ( ( et et e e l 40 l 40 F F

age of 30 age of 30 ent ent c c er er P 20 P 20

10 10

0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 62.4% of 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Fraction of Miles in Charge Depleting Mode (--) Fraction of Miles in Charge Sustaining Mode (--)

PHEV40 VMT in PHEV40 12 charge 70 60 10 depleting

50 ) ) % % 8 mode ( ( et et e e l l 40 F F 6

age of age of 30 ent ent c c

er 4 er P P 20

2 10

19 0 0 0 5 10 15 20 25 30 35 40 45 0 50 100 150 200 250 Charge Depleting Distance (mi) Charge Sustaining Distance (mi)

Percent Consumption Reduction by Distance

HEV to Conventional PHEV20 to Conventional 50 100

45 90 ) )

% % 80 40 ion ( ion ( 70

umpt 35 umpt s s n n o o 60

in C 30 in C

ion ion 50 t t c c 25 edu edu 40 R R t t n n

e 20 e c c

r r 30 e e P P

15 20

10 10 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Daily Distance (mi) Daily Distance (mi) PHEV20 to HEV Relative to Conv. 80 ) %

( 70 on i t p m

u 60 ns o PHEV benefits over C

n 50 n i o

i HEV and Conv. tied to t 40 duc

e daily distance R t

en 30 c r e P n 20 e i nc e

er 10 f f i D

0 20 0 50 100 150 200 250 300 Daily Distance (mi)

136 Conclusions

• Simulations on sample real-world drive cycles suggests PHEV technology can dramatically reduce petroleum consumption • Benefits of a PHEV over a conventional or HEV are tied to travel behavior • A vehicle designed for all electric range in urban driving will likely provide only limited electric operation in real world applications — Still provides significant fuel displacement

Next Steps

• Repeat analysis for PHEVs designed for blended operation on urban cycle — Is the fuel consumption significantly different? • Expand application of travel survey data — Apply statistical expansion factors to represent population — Access other metropolitan data sets • Repeat simulations with alternative recharge scenarios — Opportunity recharge — Never recharge • Analyze real world in-use emissions characteristics — Daily catalyst temperatures and time of day vehicle emissions

137 Section 3.3

Title: “Using GPS Travel Data to Assess the Real-World Driving Energy Use of Plug-In Hybrid Electric Vehicles” Type: Paper Authors: Jeffrey Gonder, Tony Markel, Andrew Simpson, Matthew Thornton Date: February 2007 Conference or Meeting: To be published at the Transportation Research Board 86th Annual Meeting in Washington, D.C. Abstract: Highlights the use of GPS travel survey data for simulating the potential real-world benefits of PHEV technology

138 Using GPS Travel Data to Assess the Real World Driving Energy Use of Plug- in Hybrid Electric Vehicles (PHEVs)

TRB 86th Annual Meeting, Washington, D.C., 2007

Jeffrey Gonder National Renewable Energy Laboratory (NREL); 1617 Cole Blvd.; Golden, CO 80401 Phone: 303-275-4462; Fax: 303-275-4415; [email protected]

Tony Markel National Renewable Energy Laboratory (NREL); 1617 Cole Blvd.; Golden, CO 80401 Phone: 303-275-4478; Fax: 303-275-4415; [email protected]

Andrew Simpson National Renewable Energy Laboratory (NREL); 1617 Cole Blvd.; Golden, CO 80401 Phone: 303-275-4430; Fax: 303-275-4415; [email protected]

Matthew Thornton National Renewable Energy Laboratory (NREL); 1617 Cole Blvd.; Golden, CO 80401 Phone: 303-275-4273; Fax: 303-275-4415; [email protected]

Word Count • 4695 words, plus 5 figures and 1 table • 6195 total “words” < 7,500 “word” limit

Submitted August 1, 2006

139 Gonder, Markel, Simpson and Thornton

Using GPS Travel Data to Assess the Real World Driving Energy Use of Plug- in Hybrid Electric Vehicles (PHEVs)1

Jeffrey Gonder, Tony Markel, Andrew Simpson, and Matthew Thornton National Renewable Energy Laboratory

ABSTRACT Hybrid electric and plug-in hybrid electric vehicles provide an avenue toward improved vehicle efficiency and significant reductions in petroleum consumption in the transportation sector. Knowledge of typical driving behavior is critical for such advanced vehicle design. Detailed travel survey data collected using GPS units for 227 unique consumer vehicles in the St. Louis metropolitan area was used to assess the fuel consumption and operating characteristics of conventional, hybrid electric, and plug-in hybrid electric vehicle technologies under real-world usage patterns. In comparison to standard cycles used for certification procedures, the travel survey duty cycles include significantly more aggressive acceleration and deceleration events across the velocity spectrum, which impacts vehicle operation and efficiency. Even under these more aggressive operating conditions, a plug-in hybrid electric vehicle using a blended charge-depleting energy management strategy would be expected to consume less than 50% of the petroleum used by a similar conventional vehicle. This study highlights new opportunities for using available GPS travel survey data and advance vehicle systems simulation tools to improve vehicle design and maximize the benefits of energy efficiency technologies.

INTRODUCTION The United States (U.S.) is faced with a transportation energy problem. The transportation sector is almost entirely dependent on a single fuel – petroleum. The future of petroleum supply and its use as the primary transportation fuel threatens both personal mobility and economic stability. The U.S. currently imports nearly 60% of the petroleum it consumes and dedicates over 60% of its petroleum consumption to transportation (1). As domestic production of petroleum steadily declines while U.S. consumption continues to climb, imports will also continue to increase. Internationally, the growing economies of China and India continue to consume petroleum at rapidly increasing rates. Many experts are now predicting that world petroleum production will peak within the next 5-10 years (2). The combination of these factors will place great strain on the supply and demand balance of petroleum in the near future. Hybrid electric vehicle (HEV) technology presents an excellent way to reduce petroleum consumption through efficiency improvements. HEVs use energy storage systems combined with electric motors to improve vehicle efficiency by enabling engine downsizing and by recapturing energy normally lost during braking events. A typical HEV will reduce gasoline consumption by about 30% over a comparable conventional vehicle. This number could approach 45% with additional improvements in aerodynamics and engine technology. Since their introduction in the U.S., HEV sales have grown at an average rate of more than 80% per year. However, after 5 years of availability, they represent only 0.1% of the total U.S. vehicle fleet. There are 237 million vehicles on the road today and more than 16 million new vehicles sold each year (3). Each new vehicle (the vast majority of which are non-hybrids) will likely be in-use for more than 15 years (4). With continued growth in the vehicle fleet and in average vehicle miles traveled (VMT) even aggressive introduction rates of efficient HEVs to the market will only slow the increase in petroleum demand. Reducing U.S. petroleum dependence below present levels requires vehicle innovations beyond current HEV technology.

140 Gonder, Markel, Simpson and Thornton

Plug-in hybrid electric vehicle (PHEV) technology is an option with the potential to displace a significant portion of transportation petroleum consumption by using electricity for portions of trips. A plug-in hybrid vehicle is an HEV with the ability to recharge its energy storage system with electricity from the utility grid. With a fully-charged energy storage system, the vehicle will bias towards using electricity over liquid fuels. A key benefit of plug-in hybrid technology is that the vehicle is no longer dependent on a single fuel source. The primary energy carrier would be electricity generated using a diverse mix of domestic resources including coal, natural gas, wind, hydro, and solar energy. The secondary energy carrier would be a chemical fuel stored on the vehicle (i.e. gasoline, diesel, ethanol, or even hydrogen). PHEV technology is not without its own technical challenges. Energy storage system cost, volume, and life are the major obstacles that must be overcome for these vehicles to succeed. Nonetheless, this technology provides a relatively near-term possibility for achieving petroleum displacement. One of the key factors in assessing the potential fuel use reductions of PHEVs is to assess their fuel use relative to both conventional vehicles and other advance technology vehicles, such as HEVs. This would traditionally be accomplished using controlled chassis dynamometer testing over standardized certification cycles (i.e. Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET) and the US06 Cycle). Several past studies have evaluated the fuel economy benefits from simulated PHEVs over such certification cycles (5, 6). Although these standard test cycles are widely accepted for this type of analysis, the results are somewhat limited since the cycles do not necessarily represent actual driving behavior. One alternative option is to use real-world vehicle usage to generate vehicle driving profiles from which to accurately predict the fuel consumption benefits of advanced technology vehicles. Consideration of real-world usage is of particular importance for the evaluation of PHEVs since their fuel and electricity use are highly sensitive to cycle distance and intensity. The drawback of real-world performance evaluation is often the availability of good travel data. Typical paper-based travel surveys do not provide sufficiently detailed information to perform advanced vehicle simulations. However, the second-by-second information on vehicle position, heading and speed collected from global positioning system (GPS) technology can be used to create real-world drive cycles. GPS technology uses satellite signals to track the location of vehicles, and can enhance traditional travel survey data collection methods. The purpose of this paper is to illustrate the use of GPS travel data for assessing the fuel use of PHEV technologies in a real would context. This will be accomplished by comparing the liquid fuel and electric energy use of conventional vehicles, HEVs, and PHEVs, simulated over certification cycles with that of a simulated fleet over real world driving activity sampled from an actual urban fleet using GPS technology.

OBTAINING DRIVE CYCLES FROM GPS TRAVEL SURVEY DATA Metropolitan planning organizations (MPOs) regularly collect data in travel surveys in order to update transportation demand forecasting models and identify transportation needs and areas of traffic congestion within the survey region. These surveys typically consist of mail out/mail back travel diaries, and may also include a computer-assisted telephone interview (CATI) component. In recent years, several MPOs have begun investigating use of GPS technology in order to improve the accuracy and completeness of personal travel data collection. The first significant deployment of GPS equipment in a travel survey occurred in Austin in 1998. However, the usefulness of the data was limited due to the U.S. government’s intentional degradation of GPS data signals (known as “Selective Availability”). On May 1, 2000 President Clinton announced the termination of Selective Availability, which led to ten-fold improvements in GPS data accuracy down to 5-10 meters, literally overnight. It has only been in the relatively recent time period following this change that continually improving GPS accuracy and declining equipment costs have made it practical to include a GPS data collection component in traditional travel surveys. The East-West Gateway Coordinating Council (EWGCC) became one of the initial pioneers by including a GPS component in the 2002 St. Louis Regional Travel and Congestion Survey. The GPS

141 Gonder, Markel, Simpson and Thornton

devices in the St. Louis study were used to investigate trip characteristics of a sub-sample of participants in the larger survey. The purpose of comparing the GPS and CATI survey results was to gain insight into the extent of under- or mis-reported trips, which has long been a problem with household travel studies due to the self-reporting nature of traditional survey methods. For more details on the full survey and the GPS augment than is provided in this section, the interested reader should consult the final reports on the St. Louis survey methodology and results (7, 8). In the full St. Louis survey, 5,094 households were used to represent 968,533 households and 2,428,730 persons in the region. Households participating in the GPS augment to the survey were provided a GeoStats GeoLoggerTM data collection device for up to three vehicles in the household. To utilize the data logger the survey participant needed only to plug the power connector into the vehicle’s cigarette lighter socket, and use a magnetic mount to attach the combination GPS receiver/antenna to the roof of the vehicle. To filter out non-movement events, each GeoLogger was set to record data points for vehicle speeds greater than 1 mph at one-second logging frequencies. The data recorded included date, time, latitude, longitude, speed, heading, altitude, number of satellites, and horizontal dilution of precision (HDOP, a measure of positional accuracy). A 150 household sub-sample of the 5,094 households successfully completing the CATI survey also successfully completed the GPS portion of the survey. From these 150 households, 300 vehicles received GPS instrumentation, and 227 of these 300 vehicles recorded travel on the assigned travel day (a weekday between September 5 and December 12, 2002). To satisfy the additional use of the GPS data identified in this paper, the second-by-second speed vs. time history logged by the 227 vehicles was converted into a set of 24-hour driving profiles to describe the behavior of these vehicles over a full day. The authors then used a vehicle simulation model to predict the performance and energy consumption of a simulated vehicle operating over the driving profiles. Simulation or testing time constraints often necessitate constructing a single composite cycle to approximately represent such a large set of driving profiles. However, the rapid computational speed of the model described in the following section enables individual simulation of vehicle performance over each distinct 24-hour drive cycle. The subsequent fleet-level simulation result enhances understanding of the distribution and details of vehicle performance that a single composite cycle cannot provide. The sample distribution for the GPS portion of the St. Louis survey was designed to mirror the population breakdown in the St. Louis area as well as the full study sample distribution. However, extracting conclusions about the larger St. Louis population from the simulation results using GPS- derived drive cycles requires first applying expansion factors to appropriately weight each category of vehicle/household based on its proportion in the larger population (based on household size, location, income, etc). Because the GPS sub-sample represents such a small portion of the total population, the uncertainties associated with applying expansion factors could potentially be large. Nevertheless, analyzing vehicle performance over hundreds of real-world drive cycles can certainly provide expanded insight over solely simulating a handful of synthetic profiles. By referencing recent travel surveys that include a GPS component, large numbers of real-world simulation cycles can be extracted from data originally obtained for another purpose, thus furthering the usefulness of the survey information.

PREPARING AND RUNNING VEHICLE SIMULATIONS The driving profiles extracted from the travel survey data were used to assess the real-world performance expectations of PHEVs. The second-by-second speed profiles for each vehicle were provided as input to a vehicle systems simulation tool. ADVISORTM was the particular software tool used for the detailed vehicle simulations. This program was developed at the National Renewable Energy Laboratory with support from the U.S. Department of Energy and has been refined over many years. The tool provides sufficient detail to understand the impact of component sizing and energy management decisions yet is fast enough to analyze 24 hours of driving for a fleet of vehicles in a reasonable amount of time. ADVISOR is a vehicle systems simulation tool used to assess the fuel consumption and performance of advanced technology vehicles such as hybrid electric, fuel cell, and plug-in hybrid electric vehicles (9). It also includes models for conventional and electric vehicles. The driving profile serves as a key input along with component attributes. Starting from the acceleration demands of the driving profile,

142 Gonder, Markel, Simpson and Thornton

ADVISOR determines the operating point (torque and speed or current and voltage) of each component within the powertrain at each instant in time while accounting for component losses and limitations. ADVISOR has the ability to model a variety of powertrain configurations including parallel, series, and power-split hybrid architectures. For this study all hybrids have been assumed to be in a parallel architecture so that the engine and electric motor can both provide power in parallel to the drive shaft at any time. A previous paper (10) analyzed the various design options for plug-in hybrid electric vehicles. The two primary PHEV design parameters are the usable energy content of the battery and the rated power of the battery. All other parameters depend on the choice of battery power and energy, the vehicle attributes, and the performance constraints. For this study, the vehicles were assumed to be representative mid-size sedans (similar to a Chevrolet Malibu or a Toyota Camry) with performance that would be competitive in today’s market. Table 1 summarizes the attributes of the vehicles considered.

TABLE 1 Simulated Vehicle Attributes Units Conventional Hybrid PHEV20 PHEV40 Engine Power kW 121.7 82 79.4 81.9 Motor Power kW n/a 39 43.6 48 ESS Power kW n/a 50 47 51.8 ESS Energy kWh n/a 1.9 9.4 18.5 (total) Curb Mass kg 1429 1399 1488 1567 Fuel Economy mpg 26 39.2 54 67.4 (urban/highway) Electric Consumption Wh/mi n/a n/a 95 157 (urban/highway) All Electric miles n/a n/a 22.3 35.8 Range – urban

The increased mass of the PHEV will increases its energy consumption rate. However, the larger energy storage system allows it to use the electric drivetrain more often to provide an overall energy efficiency improvement and petroleum displacement benefit. The energy storage system and traction motor have been sized to provide sufficient power to drive the entire UDDS without the use of the engine. The distance a PHEV can drive on a particular cycle before having to turn on its engine is known as the all-electric range. The PHEV20 in this study has been sized with sufficient energy to drive ~20 miles on the UDDS without the use of the engine. Likewise, the PHEV40 has sufficient energy to drive ~40 miles on the UDDS. On other more aggressive cycles the all-electric operation will be less than as designed for urban travel since the engine will need to supplement the electric motor power output in order to follow the driving profile. The energy management strategy for the PHEVs in this study will attempt to run all-electrically (without the use of a combustion engine) as much as possible as long as the battery has sufficient energy. However, if the electric drivetrain power is insufficient to satisfy the immediate needs of the driver the combustion engine will be used to supplement the electric drivetrain. As the stored energy in the battery becomes depleted the vehicle will transition into a charge-sustaining mode, in which the engine will become the primary power source and the stored energy will be used to allow the engine to operate as efficiently as possible. This is referred to as a blended charge-depleting strategy (11) or as an electric vehicle centric strategy (12) and is intended to provide as much petroleum displacement as possible for a given set of components.

143 Gonder, Markel, Simpson and Thornton

ANALYSIS RESULTS The 227 unique driving profiles derived from the St. Louis GPS survey together represent 8650 miles of travel. Figure 1 shows the distribution of daily distance traveled for this sample data set. Approximately 5% of the vehicles traveled more than 100 miles with the one vehicle traveling 270 miles. PHEV fuel efficiency and petroleum displacement impact is strongly associated with the daily distance traveled between recharge events.

12 100 90

10 )

80 % y (

) 70

8 nc

60 ue y (% eq r nc 6 50 ue ve F

40 i eq t r a l F 4 30 mu

20 u

2 C 10

0 0

-4 -8 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 12 -1 -2 -2 -2 -3 -3 -4 -4 -4 -5 -5 -6 -6 -6 -7 -7 -8 -8 -8 -9 -9 00 00 8- 0 4 8 2 6 0 2 -1 1 12 16 20 24 28 32 36 4 4 4 5 5 6 64 68 72 76 80 84 88 9 > 96 Distance (mi) FIGURE 1 Daily driving distance distribution for 227 vehicles in the St. Louis metropolitan area.

FIGURE 2 Comparison between acceleration characteristics of real-world and standard driving cycles.

In addition to the daily driving distance, the real data provides valuable insight into driving behavior. As it relates to vehicle design, the rate of acceleration and the speed at which this acceleration occurs is critical in determining the required power capabilities of the hybrid vehicle components. In Figure 2, acceleration is plotted against speed for the entire set of vehicles in the sample real-world data set on the left and for three standard driving profiles on the right. The UDDS and HWFET are used to represent typical urban and highway driving by the Environmental Protection Agency (EPA) for the

144 Gonder, Markel, Simpson and Thornton

purposes of standardized labeling of vehicle fuel economies. The US06 cycle includes more high speed and more aggressive accelerations than either the UDDS or HWFET. EPA has proposed using results from the US06 to improve vehicle fuel economy labeling to be more representative of what consumers might expect to see in-use. This figure clearly shows that even the US06 cycle does not fully encompass the range of accelerations seen in this real-world driving sample. The four vehicles described in Table 1 were each simulated over all 227 driving profiles. Figure 3 presents a summary of the simulation results. The vertical bars are associated with the left axis and represent the percentage of the 227 vehicles in-use (driving) throughout the day. Morning, mid-day and evening peaks in usage are clearly observed. The lines represent the cumulative liquid fuel consumed over the course of the day by the entire fleet of 227 vehicles assuming all vehicles are the specified architecture. The chart suggests that HEV technology was able to reduce fuel consumption by about 29% relative to the conventional case. The PHEV20 technology reduced consumption by almost 55% and the PHEV40 reduced consumption by about 66%. The PHEVs are able to displace this level of petroleum because they attempt to use the stored electrical energy to propel the vehicle as much as possible within the component limits discussed previously. In addition to gasoline consumption, the PHEVs also consume electrical energy. The fleet consumed 1212 kWh and 1821 kWh for the PHEV20 and PHEV40 configurations respectively. Since the vehicles utilize two different energy sources, it is useful to compare the vehicle configurations on the basis of total energy costs. Assuming costs of $2.41/gallon for gasoline and $0.09/kWh for electricity (national averages for 2005) results in average operating costs of 9.1c/mile for the Conventional fleet, 6.5c/mile for the HEV fleet, 5.4c/mile for the PHEV20 fleet and 5.1c/mile for the PHEV40 fleet. The simulation results indicate that PHEVs would provide substantial petroleum displacement benefits and reduce vehicle fuel costs for this “real-world fleet.” The reduced engine use, particularly during the morning commute in Figure 3 (when emissions provide the greatest contribution to smog formation) indicates that PHEVs may also provide an emissions benefit. However, further simulations will be necessary to quantify the emissions impact of PHEV technology since vehicle emissions are highly dependent on transient and on/off engine operation.

145 Gonder, Markel, Simpson and Thornton

FIGURE 3 In-use activity pattern for 227 vehicles; cumulative fleet and average vehicle consumption results for four vehicle technologies.

Figure 3 showed the consumption results for the entire fleet of 227 vehicles as a single result. Because detailed simulations were completed for each vehicle, it is also possible to examine the specific vehicle-level simulation results. Figure 4 shows the distribution of fuel consumption values for all of the vehicles in each of the four configuration scenarios. The bars represent the percentage of the fleet of 227 vehicles that achieved fuel consumption values within the indicated consumption range (each bar represents a range of 0.5L/100km). The vertical lines represent the fuel use on standard cycles (red=US06 and green=City/Highway composite). The city and highway composite value is determined by weighting the UDDS fuel economy by 55% and the HWFET fuel economy by 45%. In addition, the PHEV certification cycle values include a utility factor of 0.35 for the PHEV20 and 0.5 for the PHEV40 in order to account for the split between charge depleting and charge sustaining operation that a once-daily charged PHEV experience based on national driving statistics (13). The weighting factors are intended translate measured certification cycle results into realistic in-use values. The first important insight from Figure 4 is that a large portion of the PHEV20 and PHEV40 vehicles have real-world fuel consumption values much lower than those predicted by standard certification cycles, whereas a large portion of the conventional and HEV results are greater than the corresponding standard cycle results. From the perspective of most drivers in this real-world fleet, this result suggest that a PHEV is likely to over-deliver on fuel efficiency expectations while conventional and HEVs will likely under-deliver. A second important observation is that for both the PHEV20 and the PHEV40 nearly all vehicles in the fleet have fuel consumption values less than all of the conventional or

146 Gonder, Markel, Simpson and Thornton

HEV vehicles. Whereas Figure 3 indicated PHEVs provide a large average fleet petroleum consumption benefit, this result suggests that the reduced petroleum consumption experienced across the range of individual vehicle real-world driving profiles. Conventional Hybrid 40 40

) ) City/Highway Composite (% (%

t City/Highway t 39.2 mpg (6.0 L/100km) e 30 e 30

e Composite e l 26 mpg (9.05 L/100km) l US06 F F f f 33.2 mpg (7.1 L/100km)

o 20 o 20

e US06 e g 24.6 mpg (9.55 L/100km) g ta ta Real-world n n

e 10 Real-world e 10 rc rc e e P P 0 0 0 5 10 15 20 25 0 5 10 15 20 25 Fuel Consumption (L/100km) Fuel Consumption (L/100km) PHEV20 PHEV40 40 40

) City/Highway ) City/Highway % Composite % ( (

t t Composite 54 mpg (4.35 L/100km) e 30 e 30 67.4 mpg (3.49 L/100km) e e

Fl US06 Fl US06 39.6 mpg (5.94 L/100km) of 20 of 20 51.5 mpg (4.57 L/100km) ge ge a a t Real-world t Real-world n n

e 10 e 10 c c r r Pe Pe 0 0 0 5 10 15 20 25 0 5 10 15 20 25 Fuel Consumption (L/100km) Fuel Consumption (L/100km)

FIGURE 4 Comparison of fuel consumption distributions for various vehicle architectures.

Much recent discussion of plug-in hybrids has focused on the ability of the vehicles to operate without the use of the combustion engine (to maximize the previously defined “all-electric range” of the vehicle). As stated for this analysis, the PHEV20 and PHEV40 were respectively designed to provide 20 miles and 40 miles of all-electric range capability on the UDDS cycle. Since the examined real-world cycles were shown in Figure 2 to be much more aggressive than the UDDS, the actual in-use all-electric range could be substantially less than the designed 20 and 40 mile distances. Figure 5 confirms that a large percentage of the vehicles achieved less than 5 miles of all-electric range over the real-world cycles. Even so, it is important to recognize that these vehicles achieve significant petroleum displacement without necessarily realizing substantial all-electric range.

147 Gonder, Markel, Simpson and Thornton

FIGURE 5 In-use all-electric range performance of plug-in hybrid vehicles designed for urban cycle all-electric range.

CONCLUSIONS Relatively recent advances in GPS technology and reductions in equipment cost have enabled metropolitan planning organizations to begin incorporating a GPS component into the travel surveys they conduct. GPS helps enhance the data collected for the intended transportation planning purpose, but also presents an opportunity for new uses of this existing data due to the enhanced temporal resolution on individual vehicle driving profiles. In particular, vehicle simulation tools can utilize the resulting second- by-second drive cycles to make predictions on how different vehicle technologies will perform under the real-world driving conditions captured by the GPS survey. The speed and accuracy of modern vehicle system simulation tools, such as the ADVISOR software discussed in this paper, enable direct simulation of a range of vehicle technologies over each individual 24-hour driving profile collected in the survey. Examining performance over a range of real-world cycles provides insights beyond only conducting a small number of simulations over “standard” cycles or an aggregate cycle intended to “represent” the spectrum of real-world cycles collected. For the specific example of PHEV technology, the detailed GPS drive cycles can provide information on the time of day and duration of different driving behaviors, as well as where the vehicle parks when not in use. This information can be used to predict different charging scenarios that a PHEV fleet might experience throughout a given day. Further analysis (not directly discussed in this paper) could examine the implications of fleet recharging on the electrical utility grid in addition to the impacts on the vehicles themselves. Real-world fleet driving data can also help quantify the range of vehicle operation patterns and acceleration intensities. This information can help vehicle designers make more informed design decisions, such as understanding for a PHEV how much all-electric range actual drivers are likely to experience for a given motor size. Finally, the detailed fleet driving simulations can be used to predict the benefit that advanced vehicle technologies (which can be sensitive to driving type and distance) could provide in real-world use. Over the 227 drive profiles taken from the St. Louis GPS survey, the simulations in this paper predict that replacing a fleet of conventional midsize vehicles with a fleet of comparable PHEVs would provide an approximately 50% reduction in petroleum use. The next steps planned for this analysis include segregating the St. Louis drive cycle simulations by vehicle platform. Because the survey collected information on the make and model of each instrumented vehicle, it is possible to analyze the impact of replacing the conventional midsize vehicles with midsize PHEVs, the conventional sport utility vehicles with sport utility PHEVs, etc (rather than assuming all vehicles in the fleet are identical). The analysis can also go further to examine any correlation between vehicle or household attributes and driving behavior. Another key supplement to the analysis will be application of statistical expansion methods in order to define what conclusions about the larger St. Louis area can be drawn from the GPS drive cycle simulation results. Because the 227 vehicle sample is a relatively small subset of the vehicles in the area, the uncertainties accompanying any data

148 Gonder, Markel, Simpson and Thornton expansion will be fairly large. It is, therefore, also important to continue seeking out more and larger GPS data sets on which to perform similar simulations. Decreasing GPS survey costs and formation of partnerships between multiple users of the collected data will help increase the scope and quality of the GPS driving information and subsequent results expansion. Such expanded use of GPS data could help more accurately predict how specific advanced technologies will benefit a particular area.

ACKNOWLEDGEMENT The authors would like to acknowledge the programmatic support of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program. Additionally, we’d like to thank Jean Wolf of GeoStats and Todd Brauer of East-West Gateway Council of Governments for providing access to the GPS travel survey data.

REFERENCES 1. U.S. Energy Information Administration. www.eia.doe.gov. Accessed July 10, 2006. 2. Hirsch, R., R. Bezdek, and R. Wendling. Peaking of World Oil Production: Impacts, Risks, and Mitigation. U.S. Department of Energy. February, 2005. http://www.netl.doe.gov/energy- analyses/pubs/Oil_Peaking_NETL.pdf. 3. Highway Statistics 2004. U.S. Department of Transportation. Federal Highway Administration. http://www.fhwa.dot.gov/policy/ohim/hs04/index.htm. 4. Davis, S. and S. Diegel. Transportation Energy Databook: Edition 24. December, 2004. 5. R. Graham, et al. Comparing the Benefits and Impacts of Hybrid Electric Vehicle Options. Electric Power Research Institute (EPRI), Palo Alto, CA, EPRI Report #1000349. July, 2001. 6. Plotkin, S., et al. Hybrid Electric Vehicle Technology Assessment: Methodology, Analytical Issues, and Interim Results. Argonne Report # ANL/ESD/02-2. October 2001. 7. Household Travel Survey: Final Report of Survey Methodology. East-West Gateway Coordinating Council. January 31, 2003. http://www.ewgateway.org/pdffiles/library/trans/travelsurvey/StLouisFinalMethodsReport.pdf. 8. Household Travel Survey: Final Report of Survey Results. East-West Gateway Coordinating Council. January 31, 2003. http://www.ewgateway.org/pdffiles/library/trans/travelsurvey/StLouisFinalAnalysisReport.pdf. 9. Markel, T., et al. ADVISOR: A Systems Analysis Tool for Advanced Vehicle Modeling. Journal of Power Sources, v. 110, 2002, pp. 255-266. 10. Markel, T. and A. Simpson. Plug-In Hybrid Electric Vehicle Energy Storage System Design. Advanced Automotive Battery Conference. Baltimore, MD. May 17-19, 2006. 11. Markel, T. and A. Simpson. Energy Storage Considerations for Grid-Charged Hybrid Electric Vehicles. IEEE Vehicular Technologies Conference. Chicago, IL. September 7-9, 2005. 12. O’Keefe, M. and T. Markel. Dynamic Programming Applied to Investigate Energy Management Strategies for a Plug-In HEV. To be published at 22nd International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium & Exposition (EVS-22). Yokohama, Japan. October 23 - 28, 2006. 13. Gonder, J. and A. Simpson. Measuring and Reporting Fuel Economy of Plug-In Hybrid Electric Vehicles. To be published at 22nd International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium & Exposition (EVS-22). Yokohama, Japan. October 23 - 28, 2006.

149 Section 4

The conclusions from these analyses are:

As PHEV technology evolves, energy management strategy will become increasingly important. It will be used to ensure satisfactory battery life, maximize petroleum displacement, gain performance improvement, and manage vehicle thermal and emissions transients. NREL’s future work will apply optimization to more varied driving scenarios and include aspects beyond fuel displacement in the objective function.

For more extensive discussion of this topic, please refer to sections 4.1, 4.2, and 4.3.

150 Section 4.1

Title: “Plug-In Hybrid Electric Vehicle Energy Storage System Design” Type: Presentation Authors: Tony Markel and Andrew Simpson Date: May 19, 2006 Conference or Meeting: Presented at the Advanced Automotive Battery Conference in Baltimore, Maryland Abstract: Discusses the system design trade-offs of battery sizing and control as they relate to cost, fuel economy, and life

151 Plug-in Hybrid Electric Vehicle Energy Storage System Design

Advanced Automotive Battery Conference by Tony Markel and Andrew Simpson National Renewable Energy Laboratory

May 19th, 2006

With support from the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy FreedomCAR and Vehicle Technologies Program

Presented at the Advanced Automotive Battery Conference held May 17-19, 2006 in Baltimore, Maryland NREL/PR-540-40237

152 The Perfect Storm

Domestic Production Domestic Consumption 20

• Petroleum consumption has steadily ) ay d / increased while domestic production has b 15 m (m

continued to decline m u

3 e n li

aso 2.5 e G al) /g ag $ r 2 e e ( v ic A r l

a Source: Hubbert Center Newsletter #99/1 R. Udall and S. Andrews n

il P 1.5 o a i t a Ret N 1 ekly

e WHAT’S OUR PLAN? W 0.5

Source: U.S. Department of Energy, Energy Information Administration 0 3 Aug 11, May 07, Jan 31, Oct 28, Jul 24, Apr 19, Jan 14, Oct 10, Jul 06, 1987 1990 1993 1995 1998 2001 2004 2006 2009

Oil Use Reduction with HEVs

Light Duty Fleet Oil Use - Impact of HEVs on Consumption

16 2005 AEO Base Case HEV Scenario 14

12 Oil use same as today! BD)

P 10 3 MBPD M on ( i

pt 8 um

6 Cons l i O 4 This highly aggressive scenario assumes 100% HEV sales from 2010 onwards… 2

0 2000 2005 2010 2015 2020 2025 2030 Year HEVs unable to reduce consumption below today’s consumption level 4 Produced using VISION model, MBPD = million barrels per day

153 Oil Use Reduction with PHEVs

Light Duty Fleet Oil Use - Impact of PHEVs on Consumption

16 2005 AEO Base Case PHEV Scenario 14

12 Oil use reduction! BD)

P 10 4 MBPD M on ( i

pt 8 um

6 Cons l i

O PHEVs on E85 ?? 4 This highly aggressive scenario assumes 100% HEV sales from 2010 and 50% PHEV40 sales from 2020 onwards… 2

0 2000 2005 2010 2015 2020 2025 2030 Year PHEVs reduce oil consumption with a transition to electricity 5 Produced using VISION model, MBPD = million barrels per day

Recent PHEV Prototypes

DaimlerChrysler EnergyCS Plug-In Prius HyMotion Escape PHEV Sprinter PHEV

Renault Kangoo AC Propulsion Esoro AG H301 AFS Trinity Elect’road Jetta PHEV Extreme Hybrid™

154 PHEV Batteries

Johnson Controls / SAFT Cobasys

Valence Technologies Hymotion

Battery Characteristics

2000

1800 NiMH Li-ion ) 1600 /L 1400 Lower power to energy ratio (W 1200 leads to lighter, smaller, and sity 1000 less expensive energy Den 800 600 wer storage system o

P 400 200 0 0 5 10 15 20 Power to Total Energy Ratio (kW/kWh) 1400 2000

NiMH 1800 Projected 1200 Current Status

) Li-ion ) 1600 /kg 1000 Wh 1400 W /k ( $ r

( 1200 800 t s we

o 1000 Co P 600 800 ic ic f f i i c c 400 600 e e p p 400 S S 200 200 0 0 0 5 10 15 20 0 5 10 15 20 8 Power to Total Energy Ratio (kW/kWh) Power to Total Energy Ratio (kW/kWh)

155 All-Electric vs Blended Strategy

All-Electric

70 100% engine 60 motor 90% • Engine turns on when battery SOC 50 80% reaches low state of charge 40 70% • Requires high power battery and ) 30 60% ) W k % ( 20 50% ( motor wer SOC

Po 10 40%

0 30% -10 20% Blended

-20 10% 70 100% engine -30 0% 60 motor 90% 0 5 10 15 20 25 30 35 40 SOC 50 80% Distance (mi) 40 70%

) 30 60% ) W k % ( r

20 50% C ( • Engine turns on when power e SO

Pow 10 40% exceeds battery power capability 0 30%

• Engine only provides load that -10 20% exceeds battery power capability -20 10% -30 0% 0 5 10 15 20 25 30 35 40 Distance (mi) 9

Blended vs. AER Consumption Tradeoff

PHEV20 on LA92 0.4 • Reducing ESS power on i

t 0.3 p should reduce cost, m u 0.2 Electricity (kWh/mi)

ns mass, volume o Gasoline (L/mi) C l 0.1 Fue 0 • 50% reduction in 0 10 20 30 40 50 60 power still provides )

% almost all of the fuel ( 100 All- n m o

i consumption benefit t 80 eu c l Elect o du r t

e 60 e R P

n Blended Engine e

o 40 d i t r o ic

20 r Battery Limit e ump CD M w ns 0 o C

0 10 20 30 40 50 60 Po Energy Storage System Rated Power (kW) Battery

10 * CD = Charge Depleting

156 PHEV Battery Sizing Alternatives

50 AER

40 W) Blended (k

r 30 e 70% SOC Window 50% SOC Window w

Po 20 PHEV20 - Blended PHEV20 - AER 10 PHEV40 - Blended PHEV40 - AER 0 0 5 10 15 20 25 30 Energy (kWh) 11

Battery Life

• PHEV battery likely to deep-cycle each day driven: 15 yrs equates to 4000-5000 deep cycles • Also need to consider combination of high and low frequency cycling

70%

50%

4000

Data presented by Christian Rosenkranz (Johnson Controls) at EVS 20 12

157 PHEV Battery Cost Requirements for 5 Year Payback

$3,000 $1,Assu000med electricity cost = 9c/kWh NiMH PHEV20 (19,000 mi/year, 52 mi/day avg.) PHEV3.5 Li-Ion $2,500 PHEV40 EXPANDED 70% SOC HEV0

total) WINDOW FOR ALL PHEVS$500 $2,000 /kWh $

Current battery costs? $1,500 $-$4.30 / gal. PHEV5 HEV0 PHEV3.5

ecific cost ( 05 p $1,000 s PHEV20 $2.15 / gal. PHEV40 PHEV10 ttery What’s value of other benefits $500 Ba • Less trips to gas station • Being “Green” Projected battery costs •…$- 0 5 10 15 20 25 Power-to-energy ratio (kW/kWh) 13

Conclusions

• Plug-in hybrid technology can reduce petroleum consumption beyond that of HEV technology • The study highlighted some of the PHEV design options and associated tradeoffs — Expansion of the energy storage system usable state of charge window while maintaining life will be critical for reducing system cost and volume — A blended operating strategy as opposed to an all electric range focused strategy may provide some benefit in reducing cost and volume while maintaining consumption benefits • The key remaining barriers to commercial PHEVs are battery life, packaging and cost

158 Section 4.2

Title: “Plug-In Hybrid Electric Vehicle Energy Storage System Design” Type: Paper Authors: Tony Markel and Andrew Simpson Date: May 19, 2006 Conference or Meeting: Presented at the Advanced Automotive Battery Conference in Baltimore, Maryland Abstract: Discusses the system design trade-offs of battery sizing and control as they relate to cost, fuel economy, and life

159 Plug-In Hybrid Electric Vehicle Energy Storage System Design*

Tony Markel and Andrew Simpson National Renewable Energy Laboratory, 1617 Cole Blvd. Golden, Colorado 80401 USA

ABSTRACT Plug-in hybrid electric vehicle technology holds much promise for reducing the demand for petroleum in the transportation sector. Its potential impact is highly dependent on the system design and in particular, the energy storage system. This paper discusses the design options including power, energy, and operating strategy as they relate to the energy storage system. Expansion of the usable state-of-charge window will dramatically reduce cost but will likely be limited by battery life requirements. Increasing the power capability of the battery provides the ability to run all-electrically more often but increases the incremental cost. Increasing the energy capacity from 20-40 miles of electric range capability provides an extra 15% reduction in fuel consumption but also nearly doubles the incremental cost.

Introduction* consumption by about 30% over a comparable conventional vehicle.1 Since introduced, HEV The United States is faced with a transportation sales have grown at an average rate of more than energy dilemma. The transportation sector is 80% per year. However, after 5 years of almost entirely dependent on a single fuel – availability, they represent only 0.1% of the total petroleum. The continued role of petroleum as the U.S. vehicle fleet. There are 237,000,000 vehicles primary transportation fuel should be questioned. on the road today and more than 16 million new vehicles sold each year.[3] New vehicles will Today, nearly 60% of U.S. total petroleum likely be in-use for more than 15 years and the consumption is imported and results in billions of vehicle miles traveled (VMT) continues to dollars flowing to the economies of foreign grow.[4] It will be challenging to overcome the countries. More than 60% of U.S. petroleum inertia of the vehicle fleet. For instance, if every consumption is dedicated to transportation.[1] The new vehicle sold in 2011 and beyond was a domestic production of petroleum is steadily petroleum-fueled hybrid, our petroleum declining while our rate of consumption continues consumption level 10 years from now would still to increase; thus imports are expected to continue be 6% greater than the current light-duty fleet to increase. Meanwhile, petroleum consumption consumption, and it would never drop below rates in the emerging economies of China and today’s consumption level. Efficiency India are rapidly expanding. Furthermore, experts improvements of HEVs will be insufficient to believe world petroleum production may peak overcome vehicle fleet and VMT growth within the next 5-10 years.[2] The combination of expectations. these factors will place great strain on the supply and demand balance of petroleum in the near This presents a challenge of how to best displace future. as much petroleum consumption as soon as possible while incurring reasonable costs. Many Hybrid electric vehicle (HEV) technology is an industries, including polymer and pharmaceutical, excellent way to reduce our petroleum have little choice but to use petroleum. There are consumption through efficiency improvements. several alternatives to petroleum for a HEVs use energy storage technology to improve transportation fuel source. These include vehicle efficiency through engine downsizing and hydrogen, ethanol, biodiesel, and electricity. by recapturing energy normally lost during braking Hydrogen and fuel cell technology has advanced events. A typical HEV will reduce gasoline rapidly but still faces significant cost, infrastructure, and technical challenges that could limit market penetration within the next 15-20 * Employees of the Midwest Research Institute under Contract years. Both ethanol and biodiesel are used today No. DE-AC36-99GO10337 with the U.S. Dept. of Energy have authored this work. The United States Government retains and the publisher, by accepting the article for publication, 1 acknowledges that the United States Government retains a non- With additional improvements in aerodynamics and engine exclusive, paid-up, irrevocable, worldwide license to publish or technology, hybrid vehicles today have demonstrated upwards reproduce the published form of this work, or allow others to of a 45% reduction in consumption as compared to a do so, for United States Government purposes. conventional vehicle.

160 and help displace petroleum. However, at current All-electric range (AER) – The total distance production levels and given future expectations on driven electrically from the beginning of a cellulosic production potential, biofuels have driving profile to the point at which the engine limited ability to end our oil addiction alone but first turns on. may be more successful when combined with Electrified miles – Is the sum of all miles driven other displacement technologies. with the engine off including those after the engine first turns on. Plug-in hybrid electric vehicle (PHEV) technology PHEVxx – A plug-in hybrid vehicle with sufficient is an option with the potential to displace a energy to drive xx miles electrically on a significant portion of our transportation petroleum defined driving profile usually assumed to be consumption. A plug-in hybrid vehicle is an HEV urban driving. The vehicle may or may not with the ability to recharge its energy storage actually drive the initial xx miles electrically system with electricity from the electric utility depending on the control strategy and driving grid. With a fully charged energy storage system, behavior. the vehicle will bias towards using electricity over SOC – State-of-charge of the energy storage liquid fuels. A key benefit of PHEV technology is system. The fraction of total energy capacity that the vehicle is no longer dependent on a single remaining in the battery. fuel source. The primary energy carrier is Degree of hybridization – The fraction of total electricity generated using a diverse mix of rated power provided by the electric traction domestic resources including coal, natural gas, drive components. wind, hydroelectric, and solar energy. The Utility factor – A measure of the fraction of total secondary energy carrier is a liquid fuel (e.g., daily miles that are less than or equal to a gasoline, diesel, or ethanol). specified distance based on typical daily driving behavior. PHEV technology is not without its own technical challenges. Energy storage system cost, volume, Potential Benefits of PHEVs and life are major obstacles that must be overcome for these vehicles to be viable. The fuel A key reason for exploring PHEV technology is its displacement potential of a PHEV is directly ability to achieve significant petroleum related to the characteristics of the energy storage consumption reduction benefits. A PHEV has system. More stored energy means more miles that essentially two operating modes: a charge- can be driven electrically. However, increasing sustaining mode and a charge-depleting mode. The energy storage also increases vehicle cost and can total consumption benefits of a PHEV are a present significant packaging challenges. Finally, combination of the charge-depleting and charge- the energy storage system duty cycle for a PHEV sustaining mode improvements. is likely to be more severe from a life standpoint than electric vehicles or HEVs. Figure 1 highlights the relative importance of these two modes in achieving fuel displacement. It The purpose of this paper is to expand on the shows the total consumption benefit as a function current understanding of the potential benefits, the of the improvement in charge-sustaining mode design options, and the challenges related to consumption for HEVs and PHEVs with several PHEV technology. electric range capabilities. Several current model hybrid vehicles are included. Today’s HEVs do PHEV and HEV Terminology not have a charge-depleting mode, so their total consumption benefits are derived solely from Charge-sustaining mode – An operating mode in improvements in the charge-sustaining mode. The which the state-of-charge of the energy large dots on the plot present three scenarios that storage system over a driving profile may achieve 50% reduction in total petroleum increase and decrease but will by the end of consumption. A PHEV40 that consumed no the cycle return to a state with equivalent petroleum (all-electric operation) in charge- energy as at the beginning of the period. depleting mode with a fuel economy in charge- Charge-depleting mode – An operating mode in sustaining mode equivalent to a conventional which the state-of-charge of the energy vehicle would consume 50% less petroleum storage system over a driving profile will have because the first 40 miles of driving would be a net decrease in stored energy. done electrically. Likewise, a PHEV20 that

161 100%

90% PHEV40 PHEV20 HEV +0% cs mpg +40% cs mpg+100% cs mpg 80% oleum ) r

t 70% % e PHEV60 P

l 60% a ion ( 50% PHEV40 Prius (Corolla) 40% Escape on in Tot

i Civic Infeasible region for t PHEV20 Consumpt 30% HEV technology Accord 20% Highlander Reduc Vue 10% cs=charge sustaining HEV 0% 0% 20% 40% 60% 80% 100% Reduction in Charge-Sustaining Mode Petroleum Consumption (%) Figure 1: HEV and PHEV Fuel Consumption Benefits by Operating Mode consumed 30% less petroleum in charge- a way that allows the results to be extrapolated and sustaining mode would also consume 50% less applied to the national fleet. total petroleum. An HEV would have to achieve 90 50% reduction in consumption in its charge- 80 Percentage of Trips sustaining mode to have an equivalent total 70 Utility Factor benefit. It is unlikely that the consumption 60 reduction in charge-sustaining mode can be reduced beyond 50% cost effectively. Quantifying 50 the relative costs of adding electric range 40 capability versus improving charge-sustaining 30 mode efficiency is important. Moving vertically in 20 the figure at a given charge-sustaining mode 10 consumption level results in more miles driven 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 electrically. Electrification of miles through Trip Distance (mi) charge-depleting operation in a PHEV is expected Figure 2: 1995 NPTS Data on Daily Driving to be a cost-effective way to continue to reduce Distance Distribution and Resulting Utility Factor fuel consumption beyond HEV technology capabilities. PHEVs take advantage of the fact that the typical daily driving distance is on the order of 30 miles. The conclusions drawn from Figure 1 are based on If most of these miles could be driven electrically, national driving statistics shown in Figure 2. a large portion of our petroleum consumption Figure 2 is a histogram showing the daily driving would be eliminated. distance distribution and the resulting utility factor derived from the 1995 National Personal Transportation Survey (NPTS) data. The utility Design Options and Implications factor represents the fraction of total daily VMT Determination of the energy storage system that are less than or equal to the said distance. The characteristics is a critical step in the PHEV design utility factor is important for PHEVs because it process. The energy storage system design can be used to effectively weight the value of the variables include the power, energy, and usable charge-depleting fuel consumption benefits versus state-of-charge (SOC) window. These three the charge-sustaining fuel consumption benefits in

162 variables will affect cost, mass, volume, life, fuel 50% depth of discharge on a daily basis. Assuming economy, and vehicle operation. a 70% usable SOC window for a PHEV20 then requires 8.6 kWh of total energy capacity. This The usable energy capacity of the energy storage battery would have 5-10 times more energy system is defined by the desired electric range capacity relative to that found in current hybrid capability. The fuel displacement potential is vehicles. The PHEV40 will need 17.2 kWh. To directly related to the electric range capability. minimize total energy storage capacity (and thus From Figure 2, a range capability of 20 miles (i.e., cost and volume), it will be important to maximize a PHEV20) would substitute electrical energy for the usable SOC window for PHEVs while petroleum consumption in 30% of total VMT. satisfying cycle life requirements. Likewise, a PHEV with 40 miles of range capability could displace 50% of total VMT. A The energy storage system cost, mass, and volume typical midsize sedan will require ~300 Wh/mi for are strong functions of the energy storage system all electric operation. Thus a PHEV20 would power to energy ratio. Representative specific require ~6 kWh, and a PHEV40 would require ~12 power and power densities are provided for both kWh of usable energy. It is possible to reduce the Li-ion (based on Saft products [6]) and NiMH usable energy requirement through aerodynamic (based on Cobasys products [7]) technologies in and lightweight vehicle designs but not Figures 4 and 5. Current and projected specific substantially. cost relationships are provided in Figure 6. The cost projections are those suggested by Electric For design purposes, the usable SOC window Power Research Institute.[8] For fixed energy relates the total energy capacity to the required storage capacity, as power to energy ratio usable energy capacity. A PHEV is likely to incur decreases so do cost, volume, and mass. The at least one deep discharge cycle per day and as a question is how does reducing the power result will need to provide 4000+ deep discharge capability affect the fuel consumption reduction cycles in its 10-15 year lifetime. Figure 3 is a potential? curve-fit to data presented by Rosenkranz showing 1400 the expected cycle life performance of lithium-ion NiMH (Li-ion) and nickel-metal hydride (NiMH) 1200 Li-ion technology as a function of the discharge depth.[5] /kg) 1000 W

It shows that when a battery is discharged more r ( e 800 w

deeply, the cycle life decreases. The horizontal, o

P 600 shaded box is the typical depth of discharge c i if cycling that HEV batteries today incur while the 400 ec p

vertical, shaded box is the range of cycles that a S PHEV battery will need to endure for a 10-15 year 200 vehicle life. 0 0 5 10 15 20 100 Power to Total Energy Ratio (kW/kWh) Typical PHEV Cycles 90 During Life of Vehicle ) NiMH Figure 4: Typical Specific Power of Energy

% 80 ( Li-ion Storage Technologies 70

arge 2000 60 1800 NiMH sch

i 50 Li-ion Typical HEV ) D 1600

40 Operating Window /L

W 1400 h of 30 t y ( p 1200 it e 20 s D

n 1000

10 e 800 0 r D e 600 0 10000 20000 30000 40000 w

Number of Cycles o P 400 Figure 3: Cycle Life Characteristics of Varta 200 Energy Storage Technologies [5] 0 0 5 10 15 20 Power to Total Energy Ratio (kW/kWh) The data indicate that NiMH can achieve 4000 cycles when discharged to 70% depth of discharge Figure 5: Typical Power Density of Energy repeatedly. To achieve the same number of cycles, Storage Technologies Li-ion technology could only be discharged to

163 2000 shown by the dashed line in Figure 9. A PHEV on 1800 Projected the UDDS would need 40 kW of battery power Current Status )

h 1600 while it would require more than 60 kW on the

W 1400 LA92 cycle. Adding battery power beyond the /k $

( 1200 peak power requirement is unlikely to provide st

o 1000 additional value. Acceleration requirements will

c C 800 i place additional constraints on the energy storage f 600

eci system power requirements depending on engine p 400 S sizing. 200 150 0 US06 0 5 10 15 20 Power to Total Energy Ratio (kW/kWh)

Figure 6: Typical Specific Cost of Energy Storage ) W

k 100

Technologies ( r e w To achieve a desired all-electric range (AER) LA92 Po k

capability, the energy storage system and motor a 50 UDDS will need to provide sufficient power to propel the Pe vehicle without assistance from the engine. On an HWFET urban driving profile, the peak power is ~40 kW 0 and the power is typically less than 15 kW as 0 10 20 30 40 50 60 shown in Figure 7 for a typical light-duty vehicle. Distance (mi) Figure 8: Peak Power Requirements for All- 60 2 Electric Range Capability for Typical Mid-size Car on Several Duty Cycles Peak Power 40 1.5 20

Energy h) W W)

k US06 (k ( r 20 1

gy 15 LA92 we er h) n HWFET Po E W UDDS 0 0.5 (k 10 gy

Power er n E -20 0 0 2 4 6 8 5 Distance (mi) Figure 7: Power and Energy Requirements for All- 0 Electric Range Capability on Urban Driving 0 10 20 30 40 50 60 Distance (mi) In Figures 8 and 9, the power and energy required Figure 9: Usable Energy Requirements for All- for all-electric range capability on several driving Electric Range Capability on Typical Driving profiles is provided. The Urban Dynamometer Profiles for a Mid-size Car Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) cycles are used by the To maximize charge-sustaining fuel economy, it is U.S. Environmental Protection Agency to desirable to minimize the power rating (downsize) represent urban and highway driving behaviors in the engine as much as possible. The engine in a reporting the fuel economies of today’s vehicles. PHEV will likely be sized to provide continuous The Unified Driving Cycle, also called LA92, and performance capability. If it is assumed that the the US06 (part of the Supplemental Federal Test vehicle must achieve a continuous top speed of Procedure) are more aggressive urban and 110 mph and continuous gradeability at 55 mph on highway driving cycles respectively that are likely a 7.2% grade using 2/3 of peak engine power then to be more representative of current driving the minimum engine would need to be 80-85 kW behaviors. The energy to provide 20 miles of all- for a typical sedan. Now to achieve a 0-60 mph electric range on the UDDS and HWFET cycles acceleration time of 8.0s or less, the energy storage only provides 10 and 15 miles of range capability system would need at least 45-50 kW of power on the US06 and LA92 cycles, respectively as capability at the low SOC operating point. As a

164 result, with maximum engine downsizing, the 70 100% engine power to energy ratio would be ~5 for a PHEV20 60 motor 90% SOC and 2.8 for a PHEV40. 50 80% 40 70%

30 60% The sizes described so far are only necessary to ) W) k % ( ( r 20 50% C achieve large all-electric range capabilities. e w SO

Significant fuel can be displaced without large all- Po 10 40% electric range capability. As shown in Figure 7, the 0 30% urban drive cycle power requirements are typically -10 20% less than 15 kW. Since cost, mass, and volume of -20 10% the energy storage system can be reduced by -30 0% reducing the power to energy ratio, it is 0 5 10 15 20 25 30 35 40 Distance (mi) worthwhile to explore the fuel displacement Figure 10: Urban Cycle Operating Characteristics potential of low power energy storage systems for of an All-Electric Range Focused PHEV20 PHEVs.

70 100% The lower bound on the energy storage power will engine 60 motor 90% be a function of the lowest power to energy ratio SOC 50 80% modules available. The lowest power to energy 40 70% ratio for typical Li-ion or NiMH technology today 30 60% ) W) k % is ~1. Therefore, the minimum power will be on ( r ( 20 50% C e w the order of 10-15 kW. SO

Po 10 40%

0 30%

Employing the low-power option limits the all- -10 20% electric range capability of the vehicle. However, -20 10% if, when the engine is on, it only provides -30 0% supplemental power beyond the capabilities of the 0 5 10 15 20 25 30 35 40 energy storage system; substantial fuel Distance (mi) displacement can still be achieved via a strategy Figure 11: Urban Cycle Operating Characteristics where energy storage and engine operate in a of a PHEV20 with a Blended Strategy blended manner. The blended approach was proposed in an early paper [9] and will be referred The charts that follow summarize the tradeoffs of to as a blended strategy in the remainder of the power, energy, SOC window, and operating paper. strategy on the cost, efficiency, and fuel savings potential of a PHEV20 and a PHEV40. All Figures 10 and 11 provide a comparison between components in each vehicle scenario were sized operating characteristics for PHEVs with all- first for an all-electric range scenario and second electric-range-focused versus blended operating for a blended scenario. And for each of these four strategies. In Figure 10, the battery and motor scenarios, a 50% SOC window and a 70% usable (dashed line) have sufficient power to propel the SOC window were considered. To define the vehicle until about 22 miles at which time the blended scenario, a power to energy ratio was engine (solid line) is turned on. In Figure 11, the chosen that was half that of the all-electric range engine turns on within the first mile but when on, scenario. it only provides supplemental power, and the battery still provides most of the power. Thus, the Figure 12 summarizes the energy storage system battery discharges over approximately the same power and energy characteristics of the eight distance and displaces nearly as much fuel. vehicles considered. The SOC window only slightly impacts the power requirement while the AER case needs twice as much power as the blended case as designed. Battery energy is slightly more than a factor of two due to mass compounding.

165 60 $7,000 PHEV20 - Blended 50 $6,000 PHEV20 - AER ) PHEV40 - Blended $5,000 PHEV40 - AER

) 40 W

Cost ($ $4,000 (k

r 30 e 70% SOC Window 50% SOC Window 50% SOC Window w

ental $3,000

Po 20 70% SOC Window PHEV20 - Blended $2,000 crem

PHEV20 - AER n 10 I PHEV40 - Blended $1,000 PHEV40 - AER 0 $- 0 5 10 15 20 25 30 20% 30% 40% 50% 60% 70% Energy (kWh) Percent Reduction in Petroleum Consumption (%) Figure 12: Power and Total Energy Characteristics Figure 14: Cost-to-Benefit Relationship for of the Energy Storage System PHEVs

Incremental cost of the vehicle is likely to be a As shown in Figure 15, there are efficiency significant barrier to PHEV technology tradeoffs between a blended and an all-electric acceptance. The main reason for trying to use a range focused strategy. In the AER approach, the lower power to energy ratio energy storage system engine is as small as possible and when it is on, it would be to reduce cost while providing the same will operate at higher load fractions which amount of energy. Figure 13 shows that reducing typically correlate to higher efficiencies. The power to energy ratio and moving from an AER to energy storage system in the AER scenario is a blended strategy reduced incremental cost. higher power to energy ratio with lower internal However, increasing the usable SOC window resistance and thus less loss. On the other hand, seemed to more strongly impact the incremental the motor in the blended approach is smaller, and costs. thus running at higher load fractions with higher $7,000 efficiencies.

PHEV20 - Blended $6,000 100% PHEV20 - AER ) )

$ PHEV40 - Blended 90% ( $5,000 PHEV40 - AER PHEV20 - Blended st 80% PHEV20 - AER ency (% Co $4,000 AER 70% ci al

Blended fi f 60% ent

$3,000 E 50% SOC Window m g. 50% $2,000 cre 70% SOC Window 40% e Av n l I $1,000 30% 20% $- 0123456 10% Power to Total Energy Ratio (kW/kWh) Urban Cyc 0% Figure 13: Relationship between Incremental Cost Engine Motor Battery and Power to Energy Ratio Figure 15: Component Efficiencies for AER and Blended PHEV Strategies For a given range (e.g., PHEV20) and SOC window (e.g., 50%), moving to a lower power to The purpose of these case scenarios is to energy ratio not only reduced the incremental cost demonstrate that there are many options in the but also reduced the fuel consumption reduction design of a PHEV. Each of these options has potential as expected. The fuel consumption associated tradeoffs. Ideally, the design should benefits of the blended strategy are about 6% less find a balance between petroleum consumption than the AER strategy for both PHEV20 and reduction potential and incremental costs if it is to PHEV40 cases as shown in Figure 14. be successful. Our results demonstrate that a Interestingly, expanding the usable SOC window blended approach combined with an expanded has minimal impact on fuel consumption reduction SOC window effectively reduced cost while potential but substantially reduces incremental cost displacing nearly as much fuel in comparison to an and thus should be emphasized. all-electric range focused PHEV. These cost reductions are critical for market viability.

166 Analysis Refinements Today, the cost of hybrid battery technology is high, and tax incentives are used to make hybrids This analysis provides only a simplified view of cost competitive with comparable vehicle options. the PHEV design space and challenges. There are The energy storage system costs contributing to many uncertainties associated with these the incremental cost analysis presented earlier conclusions. In particular, there is uncertainty assume future high-volume production. Current associated with the life cycle data, the battery cost costs are estimated to be 4-5 times higher than the data, and the vehicle usage patterns. It will be long-term assumptions. Since this is a pivotal important to identify how each of these assumption, it is possible to turn the analysis uncertainties will affect the PHEV design and around and look at what battery costs might need operation. to be to provide a cost effective vehicle.

The life cycle data available at this time have been Figure 16, shows the specific costs that the energy collected as constant discharge cycles to a storage technology would need to achieve for the specified depth of discharge repeatedly. The fuel cost savings over 5 years to offset the initial batteries in HEVs today can be expected to incremental cost. The chart includes both a present encounter tens of thousands of small depth of fuel cost ($2.15/gallon gasoline and 9 ¢/kWh discharge cycles at a moderate to high SOC. electricity case) and a future fuel cost scenario PHEVs will on the other hand encounter at least ($4.30/gallon and 9 ¢/kWh). At today’s fuel costs, one deep discharge cycle on a daily basis. In to be cost neutral, PHEV20 batteries would need addition, a fully utilized energy storage system to be at the projected long-term cost goals (labeled will also encounter shallow depth of discharge as Projected Battery Costs in Figure 16). cycles both at high and low SOC levels. It has However, in the future fuel price scenario, both been assumed that the daily deep discharge cycle PHEV20 and PHEV40 energy storage systems will be the overriding factor that will determine only need to reach the $750 to $500/kWh range to life cycle performance. It is unclear how the be cost neutral respectively. shallow cycling behavior may contribute to the degradation of the energy storage system.

$3,000 Assumed electricity cost = 9c/kWh NiMH (19,000 mi/year, 52 mi/day avg.) PHEV3.5 Li-Ion

l) $2,500 a EXPANDED 70% SOC HEV0

tot WINDOW FOR ALL PHEVS h $2,000 /kW $

Current battery costs? $4.30 / gal.

cost ( $1,500 PHEV5 HEV0 PHEV3.5 cific $1,000 spe PHEV20 $2.15 / gal. ry PHEV40 PHEV10 tte a $500 B

Projected battery costs

$- 0 5 10 15 20 25 Power-to-energy ratio (kW/kWh) Figure 16: Battery Cost Requirements for Fuel Savings to Offset Incremental Cost within 5 Years

167 Additional research completed at the National References Renewable Energy Laboratory clearly shows that there is a significant connection between the 1) Energy Information Administration vehicle usage pattern and the consumption www.eia.doe.gov. reduction benefits of a PHEV both over a 2) Hirsch, R., Bezdek, R., and Wendling, R. conventional vehicle and an HEV. The analysis “Peaking of World Oil Production: Impacts, presented assumes utility factor weighted fuel Risks, and Mitigation.” February, 2005. economies based on the UDDS and HWFET 3) “Highway Statistics 2004.” U.S. Department driving profiles. It is fair to assume that neither of of Transportation. Federal Highway these driving profiles (developed in the 1970s) Administration. accurately represents typical driving habits of http://www.fhwa.dot.gov/policy/ohim/hs04/in today. In addition, the utility factor is used to dex.htm. weight the relative value of the electric range 4) Davis, S., Diegel, S. “Transportation Energy capability of a PHEV. However, the utility factor Databook: Edition 24.” December, 2004. is based on data from national personal travel 5) Rosenkranz, C. “Deep Cycle Batteries for surveys conducted in 1995. More recent data are Plug-in Hybrid Application” Presented at available and need to be analyzed. It’s likely that EVS-20 Plug-in Hybrid Workshop. Nov. 15, travel behavior is evolving. In addition, the 2003. Monaco. existing survey data typically only represent a 6) Saft website. www.saftbatteries.com. single day of the year and do not account for 7) Cobasys website. www.cobasys.com. variation daily or seasonally. PHEV benefits are 8) Duval, M. “Advanced Batteries for Electric likely to be significantly influenced by these Drive Vehicles.” EPRI Technical Report variations in driving habits. #1009299. May, 2004. 9) Markel, T. and Simpson, A. “Energy Storage Conclusions Considerations for Grid-Charged Hybrid Electric Vehicles.” IEEE Vehicular PHEVs have the potential to dramatically reduce Technologies Conference. September 7-9, future U.S. transportation petroleum consumption. 2005. Chicago, IL. To overcome the implementation challenges of PHEV technology, a systems perspective should Acknowledgements be employed. This study sheds light on the systems design tradeoffs as they relate to energy The authors would like to acknowledge the storage system technology for PHEVs. programmatic support of the U.S. Department of Specifically, it evaluates the impacts of reducing Energy Office of Energy Efficiency and power to energy ratio and expanding the usable Renewable Energy FreedomCAR and Vehicle SOC window on incremental cost and fuel Technologies Program. consumption reduction benefits.

Based on the analyses, we conclude that: • Plug-in hybrids provide potential for reducing petroleum consumption beyond that of HEV technology. • There is a spectrum of PHEV design options that satisfy performance constraints but with tradeoffs in incremental costs and fuel consumption reduction potential. • Expansion of the usable SOC window while maintaining energy storage system life will be critical for reducing incremental costs of PHEVs. • The fuel consumption reduction benefits are only slightly reduced while the battery size and cost are significantly reduced when a blended strategy is chosen relative to an all- electric range focused strategy.

168 Section 4.3

Title: “Dynamic Programming Applied to Investigate Energy Management Strategies for a Plug-In HEV” Type: Paper Authors: Michael Patrick O’Keefe and Tony Markel Date: October 2006 Conference or Meeting: To be published at the 22nd International Battery, Hybrid, and Fuel Cell Electric Vehicle Symposium and Exposition Abstract: Compares the performance of an electric-centric charge-depleting hybrid vehicle control strategy with a near-optimal dynamic programming-optimized control strategy utilizing a priori knowledge of the driving profile

169 Dynamic Programming Applied to Investigate Energy Management Strategies for a Plug-In HEV1

Michael Patrick O’Keefe National Renewable Energy Laboratory

Tony Markel National Renewable Energy Laboratory

Abstract

Plug-in hybrid electric vehicles (PHEVs) are an advanced dual-fuel powertrain technology that combine features of the battery electric vehicle (BEV) and hybrid electric vehicle (HEV). One of the fuels of the PHEV is electricity which is supplemented by another fuel (typically gasoline). The gasoline consumption for a PHEV is distance dependent based on the vehicle control strategy. In this paper, we explore two basic control concepts applied to a PHEV: an “electric vehicle centric” control strategy and an “engine-motor” blended control strategy. A near optimal control solution is derived using the dynamic programming optimization algorithm. Based on comparison with the dynamic programming results, we show that for urban driving, a PHEV should typically operate closer to an “electric vehicle centric” control strategy to provide consistently high fuel savings. We also show that PHEVs with smaller motors and lower power-to-energy ratio batteries can save nearly the same amount of fuel as a full-size PHEV—but perhaps at a reduced cost.

Keywords: Plug-In Hybrid, Hybrid Strategy, Energy Efficiency, Modeling, Dynamic Programming

1 Introduction

1.1 Plug-In Hybrid Electric Vehicles Plug-in hybrid electric vehicles (PHEVs) are a dual-fuel technology capable of transforming the transportation energy infrastructure away from non-renewable, high-carbon fuels to more environmentally responsible options. One of the PHEV fuels is electricity. The other fuel could be one of any number of options, although gasoline is considered here.

PHEVs can deliver performance equivalent with today’s modern vehicles. Furthermore, compared with other technology options, the PHEV does not suffer from some of the infrastructure issues (e.g., fuel cell vehicles) nor the limited range issues (e.g., battery electric vehicles) exhibited by other technologies. These positive benefits are the result of both efficient delivery of fuel-energy from the tank to the wheels and, more importantly, a transition from conventional transportation fuels to electricity. This is possible because PHEVs exhibit aspects of both battery electric vehicles (BEVs) and hybrid-electric vehicles (HEVs):

1 This work has been authored by an employee or employees of the Midwest Research Institute under Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States Government purposes. 170 • A large energy storage unit capable of being recharged from the electrical utility grid and supplying net motive energy over a significant distance • A hybrid powertrain typically using an internal combustion engine with an electrical motor

By blending aspects of the BEV with conventional HEVs, one can gain many of the advantages of a BEV while eliminating several disadvantages. Because fewer batteries are needed than a full BEV, the PHEV comes with a reduced cost penalty versus a BEV of equivalent performance. Furthermore, the PHEV has no range penalty and charging times are much shorter than an equivalent BEV. In contrast to an equivalent HEV, fuel consumption is further reduced since fuel energy is supplied from both electricity and liquid fuel as opposed to just liquid fuel as is the case for conventional HEVs.

PHEVs work well for vehicles that operate where relatively short trips comprise the bulk of distance traveled. By recharging between these short trips, a large portion of the motive energy can come from the electrical grid as opposed to gasoline or other fossil fuels. The transition from today’s petroleum- based transportation fuels to electricity opens up many opportunities. By recharging the vehicle’s batteries overnight, electrical utilities can increase their operating efficiency. Furthermore, due to the difference between peak capacity and base-load capacity, power utilities will typically have enough excess capacity at night to recharge a large number of vehicles before having to add new capacity. If done correctly, this can lead to reductions in carbon dioxide (CO2) and other greenhouse gas emissions. Furthermore, if effort is made to transform the power plants within the national electrical grid to use more renewable sources of energy, the benefits of renewable energy can be brought to the transportation sector through PHEVs.

However, PHEVs do have some challenges for commercialization. Chief among these are cost which is largely connected with batteries and battery life, in addition to the added cost of PHEV power electronics and powertrain components.

In this paper, we will explore two separate PHEV architectures. Both vehicles yield equivalent performance and have the same electrical capacity in the vehicle energy storage system. The first, referred to as the full-size PHEV, contains a high-power motor and energy storage unit with smaller internal combustion engine (ICE). The second vehicle, referred to as the half-size PHEV, uses a lower power motor with lower power energy storage and larger ICE. The second vehicle represents a lower- cost PHEV solution because it deemphasizes the (expensive) electric powertrain components such as high-powered batteries and emphasizes ICE technology.

In addition to the vehicle architectures, we also discuss two different PHEV energy management strategies: an “electric vehicle centric” approach and a blended-control approach. In the “electric vehicle centric” approach, the motor and batteries attempt to meet all traction demand electrically, with the engine only helping when the motor is not powerful enough. This strategy uses electricity whenever possible. The blended control approach attempts to spread the electrical consumption over a larger distance by blending engine power with motor usage at times when the system is more efficient.

In order to compare these control paradigms, the dynamic programming algorithm, which can determine the near-optimal solution of any control problem, is used.

1.2 The Dynamic Programming Method Dynamic programming is a numerical technique that can be applied to any problem that requires decisions to be made in stages with the objective of finding a minimal penalty decision pathway [1]. “Penalty” used in this sense refers to a quantitative measure of the undesirable outcomes of a decision. Dynamic programming combines knowledge of the immediate penalty of the decision at hand with knowledge of future penalties that arise as a result of the immediate decision. This algorithm has been applied with success to HEVs in the past [2], though the authors are unaware of any application to PHEVs to date.

171 Dynamic programming requires the definition of a discrete time dynamic system (DTDS) and a penalty function. Because the dynamic programming algorithm is quite computationally intensive, a fast computational model is desired for the DTDS. In the context of this paper, the DTDS is a vehicle model which calculates the change in state (battery state-of-charge) resulting from a given control setting (the engine shaft out power) over a time-step along a duty cycle. The duty cycle, the vehicle’s commanded speed versus time, is treated as deterministic for this study. The algorithm proceeds from the end of a duty cycle to the beginning, calculating the penalty of possible control settings at each time step. Because knowledge of the duty cycle is required beforehand, the dynamic programming algorithm cannot be implemented in actual control systems in real life. However, outputs from the dynamic programming algorithm can be used to formulate and tune actual controllers.

The penalty function used in this study attributes a penalty for using fuel, not meeting the specified duty cycle speed-time trace, and for not holding end state-of-charge at a reasonable level.

The dynamic programming algorithm as used in this study only explores a subset of the entire design space. Because of this, the control cannot be said to be “optimal,” only “near-optimal”.

There are two main reasons to employ the dynamic programming method in this study:

• To compare PHEV architectures under “near-optimal control” • To gain insights into what the “optimal” control is for a PHEV under various circumstances.

By comparing all PHEV architectures under an “optimal control,” control itself is eliminated as a design variable from the problem.

2 Analysis Overview This study compares the energy implications for two PHEV architectures over multiple urban duty cycles (driving patterns) and multiple distances using a “near-optimal” control strategy derived via the dynamic programming algorithm. Both vehicles contain enough electrical energy to drive approximately 32 km (5.5 kWh usable capacity). The selection of this capacity and specific sizes is based on a cost benefit analysis of PHEVs conducted by Simpson [3]. Both vehicles in this study use a parallel hybrid design where the engine and/or motor can contribute to tractive effort at any time. Gasoline is assumed for the liquid fuel in this study. However, it is important to note that PHEVs could use other fuels such as diesel, ethanol (E85), or even hydrogen if the ICE is properly designed to handle the given fuel.

2.1 Vehicle Platform, Performance, and Assumptions The vehicle platform used for this study is a mid-size sedan with performance requirements specified so as to be competitive in the North American marketplace. The specific requirements are given below in Table 1.

Table 1: Mid-Size Sedan Performance Requirements and Platform Assumptions

Attribute Value Top Speed 177 km/hr (110 mph) top speed to be maintained Full Acceleration 8.0 seconds time from 0 km/hr to 96.6 km/hr (60 mph) Passing Acceleration 5.3 seconds time from 64.4 km/hr (40 mph) to 96.6 mph (60 mph) Hill Climbing 6.5% grade @ 88.5 grade (percent rise over road-surface run) to climb with engine at 66% of km/hr (55 mph) rated power 172 Range 643.7 km (400 miles) maximum distance traveled starting fully fueled Glider Mass 905 kg the mass of the vehicle minus the powertrain Cargo Mass 136 kg the mass of cargo carried while meeting performance constraints Accessory Loads 0.7 kW electric average the accessory loads assumed for the PHEV 4.0 kW electric peak Transmission Efficiency 85% efficiency of the mechanical gearing between motor/engine and wheels Electrical Generation Efficiency for Accessories 85% efficiency of generating the power to electrical accessories

These requirements imply minimum component sizes. The peak accessory loads are assumed to be engaged for purposes of calculating the hill climbing and top speed power requirements. All other calculations (including fuel consumption calculations) assume average accessory loads. The resulting component requirements for a conventional vehicle and two PHEVs are given in Table 2.

Table 2: Component Sizes and Weights Used in Study

Component Conventional Full-Size Half-Size Vehicle PHEV PHEV Spark Ignited Internal 121.7 kW peak 80.1 kW peak 99.2 kW peak Combustion Engine 238.6 kg 171.3 kg 202.2 kg Electric Motor and NA 44.1 kW peak 21.4 kW peak Inverter 45.1 kg 33.0 kg Battery Pack NA 5.50 kWh usable 5.57 kWh usable 47.2 kW peak 23.9 kW 11.8 kWh full capacity 11.9 kWh full capacity 94.4 kg 79.6 kg Gasoline and Tank 509 kWh 396 kWh 416.7 kWh 49.1 kg 38.2 kg 40.2 kg Transmission 167.9 kg 176.0 kg 176.8 kg Support Structure 68.3 kg 78.8 kg 79.8 kg Glider Mass 905.0 kg 905.0 kg 905.0 kg Cargo Mass 136.0 kg 136.0 kg 136.0 kg Total Vehicle Mass 1428.9 kg 1508.8 kg 1516.6 kg Tested Mass 1564.9 kg 1644.8 kg 1652.6 kg Degree of 0% 35.51% 17.75% Hybridization

There are some subtleties in Table 2 that should be pointed out. First, note that advanced battery specifications are assumed. Next, the type of battery used in the full size PHEV is different from that used in the half-size PHEV. If one examines both batteries, you will quickly see that the usable capacity of both packs is nearly the same (the slight difference is due to the difference in weight and requirement for both vehicles to drive the same range). However, the weights of both packs and the pack powers are different. This arises from a difference in battery pack power-to-energy ratio. The energy density of batteries differs by power to energy ratio. Low power-to-energy ratio batteries also tend to be slightly less expensive as a technology. For more detail on how cost and weight of each component interact with vehicle requirements, see the paper by Simpson [3].

The degree of hybridization of both PHEVs appears in Table 2. This percentage is the ratio of the motor power to the engine plus motor power. The degree of hybridization of the “half-size” PHEV is half that of the full-size PHEV, hence the name.

173 2.2 Vehicle Model The vehicle model is used as the discrete time dynamic system (DTDS) by the dynamic programming algorithm. The model takes in a single control setting for each time step—the desired ICE shaft-out power for that time step. Based on the ICE shaft output power, the motor will either accept or transmit power so as to satisfy the tractive effort and accessory loads required for the given time step. The model also contains state information. The only state variable is the battery state-of-charge at the beginning of a time step. Based on the tractive effort required during the time step (defined by the duty cycle) and the control setting for ICE power, the battery state-of-charge will change.

Because the dynamic programming method is computationally expensive (requiring many model evaluations), the vehicle model used in this study has been constructed to contain only the minimum required detail so as to be quick. For example, components in the powertrain use models of power and efficiency as opposed to torque, speed, and efficiency.

A schematic of the powertrain layout and a listing of component efficiencies by output power are given for the ICE and motor/inverter components of the full-size and half-size vehicles in Figure 1.

Internal Combustion Engine for a Full-Size PHEV Internal Combustion Engine for a Half-Size PHEV

Motor & Inverter for a Full-Size PHEV Motor & Inverter for a Half Size PHEV

Powertrain Layout Internal Combustion Engine for a Conventional Vehicle

Figure 1: Component Efficiency Maps by Shaft Output Power and Powertrain Layout

174 The model is written in an open-source object oriented programming language called Python and uses the following open source modules: Numeric Python, Scientific Python, and Matplotlib [4, 5, 6]. The model does not at this time include details related to engine start-stop (i.e., durability constraints, vibration, emissions considerations).

2.2 Duty Cycles Examined The scope of this study is limited to urban driving. Two cycles, the Urban Dynamometer Driving Schedule (UDDS) and the Los Angeles 1992 (LA92) cycle are used to represent aggressive and passive urban driving. A simplification of the UDDS cycle has been substituted for the real UDDS in the interest of simulation time. The simplified UDDS cycle approximates the full UDDS using fewer time-steps, which aids in speeding up the dynamic programming algorithm. This is a great time saver during dynamic programming runs using higher design space resolution. Graphs of the time-speed traces appear below in Figure 2.

Urban Dynamometer Driving Schedule Urban Dynamometer Driving Schedule (UDDS) Los Angeles 1992 (LA92) Approximation (UDDS-a)

Close-up of UDDS Cycle Close-up of UDDS-a Cycle

Figure 2: Drive Cycles Used in this Study

3 Analysis Results The gasoline fuel consumption predicted by the models over the LA92 and UDDS-a cycles is given below in Table 3. A lower heating value of 32 MJ/liter of gasoline is assumed. The values given for fuel economy for this mid-size sedan are reasonable for the North American market. The charge sustaining fuel consumption numbers for the PHEVs represent a best case scenario for hybrid fuel savings. Fuel savings from the PHEV relative to the conventional vehicle arise from three main sources: regenerative braking, engine-off operation for the PHEVs, and more efficient operation of the internal combustion engine by supplementing with the traction motor and battery system.

Table 3: Gasoline Fuel Consumption for Conventional Vehicle and Charge Sustaining PHEVs CYCLE Conventional Vehicle Full-Size PHEV Half-Size PHEV charge sustaining charge sustaining LA92 3512 J/m 1789 J/m 1862 J/m 10.98 L/100 km 5.59 L/100 km 5.82 L/100 km 21.43 mpg 42.07 mpg 40.42 mpg UDDS-a 3508 J/m 1535 J/m 1522 J/m 10.96 L/100 km 4.80 L/100 km 4.76 L/100 km 21.46 mpg 49.03 mpg 49.45 mpg

175 Highway Federal 2199 J/m NA NA Emissions Test (US 6.87 L/100 km EPA) 34.23 mpg

In Table 3, the values given for the highway federal emissions test (US EPA) are for the conventional vehicle only and are used here to aid the reader in gaining a feel for the range of fuel consumption.

Figure 3 shows two of the output graphs resulting from the dynamic programming algorithm being applied to the PHEV models. Here, the (near-) optimal engine command is shown versus the tractive effort required between the tires and the wheel. This power measured at the tire-road interface is referred to as the “roadload.” Two cycles are run in Figure 3: five UDDS-a cycles to the left (29.4 miles/47.3 km) and four LA92 aggressive urban driving cycles (39.3 miles/63.2 km) to the right.

The engine is not producing power (zero load) for a large portion of the duration of both cycles. When the engine is transmitting power, it appears to be supplementing the traction motor as evidenced by operating commands falling below the line y=x/0.85, where y is the engine command and x is the roadload (i.e., relating roadload to power at the engine shaft via the constant transmission efficiency of Table 1). This engine operation chosen by dynamic programming is a rather complex blending algorithm that minimizes fuel consumption within the constraints given.

We quickly see that duty cycle does make a difference when we contrast the results of the dynamic programming algorithm run over the UDDS-a with those of the LA-92 cycle (Figure 3). Due to the repetition and simplification of the UDDS-a, there is not as much point scatter. We do see similar trends of increased engine usage with roadload. However, we do not see the same amount of point scatter at low engine commands and low roadload as we do for the LA-92.

) FULL Size 60 60 W) HALF UDDSax5 W 50 FULL UDDSax5 50 HALF Size d (k 40 40

30 and (k 30 Exact

m Requirement mman 20 20 m 10 o 10

ne Co 0 0 ne C gi -60 -30 0 30 60 -60 -30 0 30 60 En ngi Roadload (kW) Roadload (kW) E Figure 3: Engine Command versus Roadload over UDDS-a x5 and LA92 x4

The engine commands versus time for the full-PHEV over five UDDS-a cycles and five LA92 cycles appear in Figure 4. There are some similarities in the approaches taken by the dynamic programming algorithm in both cases. First we see a strong emphasis on engine commands in the 20-kW to 40-kW region repeating each cycle iteration (i.e., five times over the five cycle sequence). This is a sensible approach. If it is known ahead of time that engine power must be used to supplement the electric capacity of the ESS, it is best to use the engine when the engine is most efficient. As can be seen from Figure 1, the engine is most effective when at higher power loadings (peak efficiency between 30-50 kW).

Another way to help understand more in-depth what the dynamic programming algorithm is doing is to examine the energy storage system state-of-charge or capacity versus distance. These data inform us of how the battery system is being drawn down—aggressively discharged or at a reduced discharge rate (due to a blending of battery energy with engine power).

Figure 5 (left) shows the energy storage system discharge over three dynamic programming runs: a run of one UDDS-a cycle, a run of three UDDS-a cycles, and a run of five UDDS-a cycles. The full- size PHEV is used on all of the runs. Note that before the vehicle reaches the “electric vehicle centric” range of about 40 km (for the UDDS), the drawdown is completely along an “electric dominant”

176 drawdown path. As distance increases over this “electric vehicle centric” drawdown path, the slope of discharge with distance decreases (i.e., a longer distance is required to discharge the same amount of battery energy). Minimum gasoline consumption is obtained by spreading the battery energy out over the entire distance of the cycle (such that the desired end state-of-charge is reached when the desired range is reached).

) ) 60 60 UDDSax05 LA92x05 kW kW 50 50 ( (

40 d 40

and 30 30

20 mman 20 o

10 C 10 e e Comm n in

i 0 0 g g 0 2000 4000 6000 8000 0 2000 4000 6000 8000 En En Time (s) Time (s)

Figure 4: Engine Commands for the UDDS-a and LA92 by Time

The right-hand side of Figure 5 shows the same state-of-charge curve for the LA-92 cycle. This time, the PHEV simulations optimized by dynamic programming over 1 through 5 cycles are shown. In addition, a charge sustaining (CS) run over one LA-92 distance and a run over five LA-92 cycles where the vehicle first has an “electric vehicle centric” discharge followed by charge sustaining operation are shown. In the “electric vehicle centric” case (FULLx05 EV followed by CS operation), the vehicle discharges its electrical energy as fast as possible and then goes into charge sustaining operation. This case provides an interesting contrast to the dynamic programming run over the same distance (FULLx05).

UDDS-a LA92 FULL x01 (bold) 1.0 1.0 FULLx01 FULL x05 EV 0.8 FULLx03 0.8 followed by FULLx05 CS operation FULL x03

0.6 arge 0.6

UDDSa h FULL x04

C 0.4 0.4 FULL x05 FULL x02 0.2 e of 0.2 FULL x01cs ate of Charge t at t S

0.0 S 0.0 0 10203040506070 0 20406080 Distance (km) Distance (km)

Figure 5: SOC by Distance over UDDS-a and LA92 cycles

In the right-hand-side of Figure 5, for the first couple of LA92 cycles, the vehicle has not yet reached its “all-electric range”. As such, the PHEV can operate in an “electric vehicle centric” mode relying almost exclusively on battery energy. For distances below the “all-electric range”, the dynamic programming algorithm operates the PHEV with an “electric vehicle centric” discharge where possible because this type of operation minimizes gasoline consumption. When the vehicle is asked to operate beyond a range that can be supplied exclusively by battery energy (i.e., beyond the all-electric range), the dynamic programming algorithm blends engine operation with battery discharge to minimize gasoline usage while still driving the requested distance.

In contrast to the dynamic programming strategies for distances above the “all-electric range”, the “electric vehicle centric” strategy draws down as fast as possible and then goes into a charge

177 sustaining mode. Note that charge sustaining operation is around 5% of full (usable) SOC. The results in Figure 5 were exclusively for the “full-sized” PHEV. Let us examine the effect that component sizing and powertrain architecture can have on consumption rates.

Figure 6 shows the consumption rates of gasoline and electricity for the full-size and half-size PHEVs over repeats of the UDDS-a and LA92 cycles. The values for electricity consumption for both full and half-size architectures are quite similar after all-electric range is exceeded (around 40 km on the UDDS-a). This is because both vehicles discharge all of their capacity (albeit in different ways) over the same distance. Electricity consumption also begins to decrease after all-electric range is exceeded. This is because a fixed electrical capacity is being spread out over longer and longer distances. In contrast, gasoline consumption increases after exceeding all-electric range. Note that the full-size PHEV does not require gasoline at any point below its all-electric range for the UDDS-a cycle. On that same UDDS-a cycle, full-size gasoline consumption rates exceed half-size PHEV gasoline consumption rates at higher distances. This is not the case under more aggressive driving such as what is seen on the LA92. The UDDS-a results are due to the smaller motor in the half-size PHEV being better utilized and thus more efficient. Note that fuel consumption rates are not very different between the full and half-size PHEVs for most distances. Full-size and half-size electricity consumption rates are nearly identical after all-electric range is met. The largest disparity is under aggressive urban driving prior to all-electric range being met. However, in terms of absolute fuel usage, the fuel usage of the half-size PHEV is still only about 1/9th the consumption rate of a conventional vehicle over the same cycle.

UDDSa LA92 4.0 4.0 FULL Gasoline

FULL Electric , , ) ) FULL Electricity

m FULL Gasoline m km k

3.0 km 3.0 HALF Gasoline k HALF Electric on 0 HALF Gasoline i HALF Electricity t 10 10 100 10 / tion / p y/ y t p t 2.0 2.0 ne/ ne i ci i ci i m r ri t sol 1.0

1.0 ec ect l onsum h e C h el ers ga Consu ers gasol 0.0 t

t 0.0 i i l kW l kW ( ( 0 20406080 0 20406080 Distance (km) Distance (km) Figure 6: Fuel Consumptions of PHEVs over UDDS-a and LA92 Cycles

Figure 7 shows us one of the key areas of impact for PHEV technology—gasoline fuel usage by distance. The set of graphics in Figure 7 are run for the half and full-size PHEV vehicle over the UDDS-a (mild urban driving) cycle. Each curve on the figure is the result of a dynamic programming run optimized for the given distance. That is, for example, the curve labeled “HALFx3” is the fuel consumption minimized by the dynamic programming algorithm to have the lowest net fuel consumption after 3 UDDSa cycles back-to-back. This distance aspect is key to understanding what is going on with the dynamic programming control of PHEVs. In this case, the dynamic programming algorithm has a priori knowledge of the cycle and distance to be run and shows us the optimum control under that circumstance. For distances above the “all-electric range,” the operation is a “blending strategy” that blends engine usage with motor usage so as to minimize fuel consumption while meeting the drive cycle trace. This blending is in addition to that required due to component size limitations such as when an acceleration event requires more power than the traction motor can handle in isolation and thus the engine assists.

178 HALF-SIZE FULL-SIZE 1.0 1.0 HALFx05 CS FULLx05 CS

ers) 0.8 ers) 0.8 t t

i HALFx01 HALFx05 i FULLx01 FULLx05 l 0.6 l 0.6 HALFx04 FULLx04 0.4 0.4 HALFx03 FULLx02 0.2 0.2 HALFx02 FULLx03 0.0 0.0

Consumption ( 0 20406080 Consumption ( 0 20406080 Distance (km) Distance (km)

Figure 7: Gasoline Consumption of Half and Full-Size PHEVs over UDDS-a Cycles

What is key to note here is that for both the half and full-size PHEVs, a fuel consumption optimally blended to have minimum fuel consumption at a given target distance does not necessarily have the minimum consumption at other distances. Figure 8 further expands upon this point by displaying gasoline consumption results for the LA92 contrasted with an “electric vehicle centric” control strategy. All runs change to charge sustaining operation after reaching their target distances. The charge sustaining control is optimized by dynamic programming to minimize fuel.

HALF-SIZE FULL-SIZE

n HALFx03 5 5 FULLx03 o

i HALFx04

ion FULLx04 pt 4 HALFx05 t 4

p FULLx05 HALFx05 EV FULLx05 EV ) 3 3

s Cycle End Points Cycle End Points r e

t 2 2 ers) li ( it

1 (l 1 0 0 0255075100

Gasoline Consum 0 255075100

Distance (km) Gasoline Consum Distance (km)

Figure 8: Gasoline Consumption over LA92 for Half- and Full-Size PHEV

Figure 8 shows the results of dynamic programming runs applied to multiple distances of the LA92 driving cycle. The designation “FULLxN” refers to the full-size PHEV optimized by dynamic programming to run for a number of LA92 cycles equal to N. Dynamic programming for N cycles operates the vehicle to have near minimum fuel consumption for the given distance.

Figure 8 also contains the results of the PHEV run in an “electric vehicle centric” mode (labeled “EV” in the figure) where the engine is commanded to be off unless doing so would cause the vehicle to not meet the cycle speed-time trace. Thus, simply put, “use electricity to power the vehicle if at all possible and supplement with the engine if the motor does not have enough power to meet the cycle speed-time trace.” When the vehicle has run its target distance, the PHEV begins to run in charge sustaining mode. That is, the PHEV begins to run similar to a conventional HEV where no significant net discharge of the energy storage system occurs over time or distance. This can be seen clearly for the “FULLx03” case where the vehicle has one of the lowest energy consumptions of the “blended” modes until one LA92 distance and then changes over to charge sustaining mode. As can be seen from Figure 8, gasoline fuel consumption for the “electric vehicle centric” control is nearly equivalent in all cases to the dynamic programming control set to minimize fuel consumption over a specific distance. This is because we are continually running the same cycle back-to-back and thus it doesn’t matter if one uses the electrical energy all at once at the beginning or spread out

179 throughout the cycle. The equivalency between strategies would not necessarily be the case for varied driving patterns though the effect of driving pattern variation has not been examined in this study.

Now, let us consider these dynamic programming results in the context of what can be practically implemented in real life. There are two general strategies that have been presented: a “blended” engine/motor strategy and an “electric vehicle centric” strategy. The “blended” mode is what the dynamic programming algorithm optimized to give minimum fuel usage for a given distance most closely resembles. The engine is run at the most opportune times to minimize fuel consumption. In the “electric vehicle centric” strategy, focus is given to supplying transportation energy with electricity and the engine is only used when necessary to meet vehicle performance constraints.

Note that for distances below the PHEV’s electric range, the dynamic programming algorithm chooses an “electric vehicle centric” strategy as this minimizes fuel usage for any range below the electric range. That is, if we know we will be running less than the all-electric range of the vehicle, the “electric vehicle centric” strategy is near optimal. For distances longer than the PHEV’s electric range, a blended strategy is chosen by dynamic programming. However, let us consider the “cost of being wrong” in terms of choosing a control strategy for trip distances above the electric range of the vehicle. Figure 8 shows us that a vehicle with a “blended” strategy optimized for an intended 80 km of intense urban driving would use more fuel than the “electric vehicle centric” strategy all the way up to above 70 km of driving distance. It should be noted that the “electric vehicle centric” control strategy benefits from a charge sustaining control optimized by dynamic programming. However, so does the actual blending mode control. Thus, even if we imagine adjusting the fuel consumption rates upwards during both blended charge depleting operation and charge sustaining operation, the point is still clear: a vehicle operating in blended mode that deviates from the target distance uses more fuel than a vehicle using an “electric vehicle centric” approach. Therefore, the “electric vehicle centric” mode is essentially the optimal fuel consumption case over most of the distance. In contrast, the “blended” mode is optimal for specific distances but non-optimal for others.

4 Conclusions This study shows that a half-size PHEV using a smaller motor and low power-to-energy ratio batteries has nearly the same fuel consumption of a full-size PHEV (and in some cases, can have an even lower fuel consumption), but uses components that can be of lower cost. The biggest disparity in gasoline fuel consumption rates is at low distances (below the all-electric range of the PHEV) over aggressive driving cycles where the engine is often forced to assist when meeting roadloads. Even so, under these conditions, the absolute fuel usage is low (~1/9th the fuel consumption of a conventional vehicle for the case of the LA92 cycle).

Furthermore, this study shows that under optimal control, a blended control strategy uses approximately the same amount of fuel as an “electric vehicle centric” approach for known target distances (and constant driving patterns). However, the penalty for “guessing wrong” on the target distance and type of travel can be high for a blended strategy. That is, a control strategy optimally blended to have minimum fuel consumption at a given target distance does not necessarily have the minimum consumption at other distances. Thus, it is typically better to run with an “electric vehicle centric” control strategy that emphasizes using electricity to supply vehicle power demand to the extend possible within the limits of the motor size.

This work was conducted over urban driving cycles for PHEVs with specific energy capacities. Further work should cover other energy capacity sizes and types of driving including the effect of varied driving patterns along a given trip. Additionally, the details of engine on/off including cranking energy and emissions implications should be addressed.

180 References [1] Bertsekas, Dimitri P., Dynamic Programming and Optimal Control, Athens Scientific, 2000 [2] Lin, Chan-Chiao; Peng, Huei; and Grizzle, Jessy W., Control System Development for an Advanced- Technology Medium-Duty Hybrid Electric Truck, Society of Automotive Engineers 2003-01-3369, 2003 [3] Simpson, Andrew, Cost-Benefit Analysis of Plug-in Hybrid Electric Vehicle Technology, EVS-22, 2006 [4] Python Programming Language, http://www.python.org [5] Numeric Python and Scientific Python, http://www.scipy.org [6] Matplotlib Python Extension, http://matplotlib.sourceforge.net Authors

Michael Patrick O’Keefe, Senior Research Engineer, National Renewable Energy Laboratory, 1617 Cole Blvd; Golden, CO 80401 USA; Phone: 303.275.4268; Fax: 303.275.4415; e-mail: [email protected]. 2000-Present NREL; 1997-2000 MS Mechanical Engineering and MS Inter-engineering: Technical Japanese, University of Washington, USA; 1998 Nippon Steel Corporation, Japan; 1992-1996 BS Mechanical from Northern Arizona University, USA

Tony Markel, Senior Research Engineer, National Renewable Energy Laboratory, 1617 Cole Blvd; Golden, CO 80401 USA; Phone: 303.275.4478; Fax: 303.275.4415; e-mail: [email protected]. 1996-Present NREL; 2002-2005 MS Mechanical Engineering from University of Colorado, USA; 1995-1996 Argonne National Laboratory; 1991-1995 BSE Mechanical Engineering from Oakland University, USA.

181 Summary

182 Form Approved REPORT DOCUMENTATION PAGE OMB No. 0704-0188 The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. 1. REPORT DATE (DD-MM-YYYY) 2. REPORT TYPE 3. DATES COVERED (From - To) November 2006 Milestone report 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Plug-In Hybrid Vehicle Analysis DE-AC36-99-GO10337

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6. AUTHOR(S) 5d. PROJECT NUMBER T. Markel, A. Brooker, J. Gonder, M. O'Keefe, A. Simpson, and NREL/MP-540-40609 M. Thornton 5e. TASK NUMBER FC06.2000

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14. ABSTRACT (Maximum 200 Words) NREL’s plug-in hybrid electric vehicle (PHEV) analysis activities made great strides in FY06 to objectively assess PHEV technology, support the larger U.S. Department of Energy PHEV assessment effort, and share technical knowledge with the vehicle research community and vehicle manufacturers. This report provides research papers and presentations developed in FY06 to support these efforts. The report focuses on the areas of fuel economy reporting methods, cost and consumption benefit analysis, real-world performance expectations, and energy management strategies. 15. SUBJECT TERMS plug-in hybrid electric vehicle; PHEV; transportation; vehicle technologies; hybrids; fuel economy; cost and consumption; real-world performance; energy management; National Renewable Energy Laboratory; NREL 16. SECURITY CLASSIFICATION OF: 17. LIMITATION 18. NUMBER 19a. NAME OF RESPONSIBLE PERSON OF ABSTRACT OF PAGES a. REPORT b. ABSTRACT c. THIS PAGE UL Unclassified Unclassified Unclassified 19b. TELEPHONE NUMBER (Include area code)

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